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Abstract:

Continuous monitoring of a plurality of physiological data may be used
for health and fitness improvements for a user. To this end, a
physiological monitoring and measurement device may include a wearable
strap that receives heart rate data for a user including a time series of
heart rate data, a maximum heart rate, and a resting heart rate. A
processor may transform the heart rate data into a time series of heart
rate reserve data that is weighted, e.g., to account for cardiovascular
efficiencies at different intensity levels, to provide a weighted time
series of heart rate reserve values. An intensity score that provides an
indicator of cardiovascular intensity for an exercise routine may be
generated from the weighted time series of heart rate reserve values and
displayed to the user on the wearable strap.

Claims:

1. A device for physiological monitoring and measurement comprising: a
continuously wearable physiological measurement device including one or
more sensors configured to continuously monitor and determine a heart
rate of a user; a display device coupled to the continuously wearable
physiological measurement device; a strap attached to the continuously
wearable physiological measurement device and couplable to an appendage
of the user; and a processor in the continuously wearable physiological
measurement device, the processor coupled to the one or more sensors and
the processor configured to: programmatically receive heart rate data for
the user from the continuously wearable physiological measurement device,
the heart rate data including a time series of heart rate data for the
user, a maximum heart rate of the user, and a resting heart rate of the
user; transform the heart rate data including the time series of heart
rate data, the maximum heart rate and the resting heart rate to a time
series of heart rate reserve data; weight the heart rate reserve data
according to a weighting scheme to provide a weighted time series of
heart rate reserve values; programmatically generate an intensity score
providing an indicator of cardiovascular intensity based on the weighted
time series of heart rate reserve values; and display the intensity score
on a user interface rendered on the display device.

2. The device of claim 1, wherein the weighting scheme is created for the
user by a machine learning algorithm trained to correlate heart rate data
to cardiovascular intensities.

3. The device of claim 2, wherein the machine learning algorithm is
retrained to adjust perceived difficulties of exercise routines as a
fitness of the user improves.

4. The device of claim 1, wherein the weighting scheme accounts for
cardiovascular efficiencies at different intensity levels.

5. The device of claim 1, wherein the processor is further configured to
display qualitative information associated with the intensity score.

6. The device of claim 5, wherein the qualitative information includes an
indication of whether the user exceeded an anaerobic threshold of the
user during an exercise routine.

7. The device of claim 5, wherein the qualitative information includes an
indication of whether the user is likely to experience muscle soreness.

8. The device of claim 5, wherein the qualitative information includes an
indication of a level of recovery required after the exercise routine.

9. The device of claim 5, wherein the qualitative information includes an
indication of one or more future alterations to the exercise routine that
is required based on one or more health-related goals of the user.

10. The device of claim 1, wherein the indicator corresponds to a
perceived difficulty of the exercise routine by the user, and wherein the
processor is further configured to: display, on the user interface, the
perceived difficulty of the exercise routine.

11. The device of claim 1, wherein the processor is further configured
to: automatically alter an exercise plan according to one or more health
goals of the user based on the intensity score; and display, on the user
interface, an altered exercise plan.

12. The device of claim 1, wherein the processor is further configured
to: programmatically receive data corresponding to heart rate of a second
user during an exercise routine; transform the heart rate data to a time
series of heart rate reserve data; weight the heart rate reserve data
according to a weighting scheme; programmatically generate a second
indicator of cardiovascular intensity based on the weighted heart rate
reserve data; and display, on the user interface rendered on the display
device, the indicator corresponding to the user and the second indicator
corresponding to the second user.

13. The device of claim 12, wherein the heart rate data of the user and
the second user are obtained from different user-selected time periods.

14. The device of claim 1, wherein the processor is further configured to
sum and normalize the weighted time series of heart rate reserve values
for a predetermined unit interval to provide a summed and normalized
value.

15. The device of claim 14, wherein generating the intensity score
includes scaling the summed and normalized value to a predetermined
distribution in a scale, wherein a path of the intensity score over time
is used to dynamically determine and adjust the weighting scheme applied
to the heart rate reserve data.

16. The device of claim 15, wherein the predetermined distribution is a
linear distribution.

17. The device of claim 15, wherein the predetermined distribution is one
or more of an arctangent distribution, a sigmoid distribution, and a
sinusoidal distribution.

18. The device of claim 15, wherein the predetermined distribution is
obtained by fitting a curve to a predetermined group of canonical
exercise routines have predefined intensity scores.

19. The device of claim 1, wherein the processor is further configured to
programmatically receive heart rate variability data from the
continuously wearable physiological measurement device.

20. The device of claim 19, wherein the processor is further configured
to calculate a recovery score for the user based on the heart rate
variability data.

Description:

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is a continuation of U.S. patent application Ser.
No. 14/326,598 filed on Jul. 9, 2014, which is a continuation of U.S.
patent application Ser. No. 14/018,262 filed on Sep. 4, 2013, which
claims the benefit of U.S. Provisional Patent Application No. 61/696,525
filed on Sep. 4, 2012 and U.S. Provisional Patent Application No.
61/736,310, filed on Dec. 12, 2012. The entire contents of each of the
aforementioned applications are incorporated herein in their entirety by
reference.

BACKGROUND

[0002] There is an increasing demand for health and fitness monitors and
methods for providing health and fitness monitoring. Monitoring heart
rate, for example, is important for various reasons. Monitoring heart
rate is critical for athletes in understanding their fitness levels and
workouts over time. Conventional techniques for monitoring heart rate
have numerous drawbacks. Certain conventional heart rate monitors, for
example, require the use of a chest strap or other bulky equipment that
causes discomfort and prevents continuous wearing and use.

[0003] This presents a challenge to adoption and use of such monitors
because the monitors are too obtrusive and/or are directed to assessing
general well-being rather than continuous, around-the-clock monitoring of
fitness. Certain conventional heart rate monitors do not enable
continuous sensing of heart rate, thereby preventing continuous fitness
monitoring and reliable analysis of physiological data. Additionally, a
challenge to adoption of fitness monitors by athletes is the lack of a
vibrant and interactive online community for displaying and sharing
physiological data among users.

SUMMARY

[0004] Embodiments provide physiological measurement systems, devices and
methods for continuous health and fitness monitoring. A wearable strap
may detect reflected light from a user's skin, where data corresponding
to the reflected light is used to automatically and continually determine
a heart rate of the user. The wearable strap may monitor heart rate data
including heart rate variability, resting heart rate, and sleep quality.
The systems may include a processing module that generates an indicator
of physical recovery based on the heart rate data. The recovery indicator
may be used to determine strain related to an exercise routine,
qualitative information on the user's health, whether to alter a user's
exercise plan, and so forth.

[0005] Embodiments provide physiological measurement systems, devices and
methods for continuous health and fitness monitoring. A lightweight
wearable system is provided to collect various physiological data
continuously from a wearer without the need for electrocardiography (ECG)
equipment or a chest strap. The system also enables monitoring of one or
more physiological parameters in addition to heart rate including, but
not limited to, body temperature, heart rate variability, motion, sleep,
stress, fitness level, recovery level, effect of a workout routine on
health and fitness, caloric expenditure, global positioning system (GPS)
location, altitude, and the like. Embodiments also include
computer-executable instructions that, when executed, enable automatic
analysis, transformation and interpretation of one or more physiological
parameters to assess the cardiovascular intensity experienced by a user
(embodied in an intensity score or indicator) and the user's recovery
after physical exertion (embodied in a recovery score). These indicators
or scores may be stored on a non-transitory computer-readable medium and
displayed on a visual display device to assist a user in managing the
user's health and exercise regimen.

[0006] In accordance with one exemplary embodiment, a wearable
physiological measurement system is provided. The wearable physiological
measurement system includes a wearable strap configured to be couplable
to an appendage of a user. The strap includes one or more light emitters
for emitting light toward the user's skin, one or more light detectors
for receiving light reflected from the user's skin, an electronic circuit
board implementing a processing module configured for analyzing data
corresponding to the reflected light to automatically and continually
determine a heart rate of the user, and a first set of one or more
batteries for supplying electrical power to the one or more light
emitters, the one or more light detectors and the electronic circuit
board. The wearable physiological measurement system also includes a
modular housing removably couplable to the strap. The modular housing
includes a second set of one or more batteries chargeable by an external
power source, the second set of batteries configured to recharge the
first set of batteries in the strap. The combination of the first and
second sets of batteries enables continuous monitoring of the heart rate
of the user by the wearable strap.

[0007] In accordance with another exemplary embodiment, a wearable
physiological measurement system is provided. The wearable physiological
measurement system includes a wearable strap configured to be couplable
to an appendage of a user. The strap includes one or more light emitters
for emitting light toward the user's skin, one or more light detectors
for receiving light reflected from the user's skin, and an electronic
circuit board comprising a plurality of electronic components configured
for analyzing data corresponding to the reflected light to automatically
and continually determine a heart rate of the user. The circuit board
includes a processing module configured to, based on one or more signals
associated with the heart rate of the user, detect an identity of a
portion of the user's body to which the strap is coupled, and to, based
on the identity of the appendage, adjust data analysis of the reflected
light to determine the heart rate of the user.

[0008] In accordance with another exemplary embodiment, a wearable
physiological measurement system is provided. The wearable physiological
measurement system includes a wearable strap configured to be couplable
to an appendage of a user. The strap includes one or more light emitters
for emitting light toward the user's skin, one or more light detectors
for receiving light reflected from the user's skin, and an electronic
circuit board comprising a processing module configured for analyzing
data corresponding to the reflected light to automatically and
continually determine a sequence of instantaneous heart rate of the user.
The processing module is configured to determine the heart rate of the
user by: executing one or more computer-executable instructions
associated with a peak detection algorithm to process the data
corresponding to the reflected light to detect a plurality of peaks
associated with a plurality of beats of the user's heart, determining an
R-wave-to-R-wave interval (RR interval) based on the plurality of peaks
detected by the peak detection algorithm, determining a confidence level
associated with the RR interval, and based on the confidence level
associated with the RR interval, selecting either the peak detection
algorithm or a frequency analysis algorithm to process data corresponding
to the reflected light to determine the sequence of instantaneous heart
rates of the user.

[0009] In accordance with another exemplary embodiment, a
computer-executable method is provided for automatically and continually
determining a sequence of instantaneous heart rate of the user. The
method includes executing one or more computer-executable instructions
associated with a peak detection algorithm to process the data
corresponding to the reflected light to detect a plurality of peaks
associated with a plurality of beats of the user's heart, determining an
RR interval based on the plurality of peaks detected by the peak
detection algorithm, determining a confidence level associated with the
RR interval, and based on the confidence level associated with the RR
interval, selecting either the peak detection algorithm or a frequency
analysis algorithm to process data corresponding to the reflected light
to determine the sequence of instantaneous heart rates of the user.

[0010] In accordance with another exemplary embodiment, one or more
non-transitory computer-readable media are provided having encoded
thereon computer-executable instructions for performing a method for
automatically and continually determining a sequence of instantaneous
heart rate of the user. The method includes executing one or more
computer-executable instructions associated with a peak detection
algorithm to process the data corresponding to the reflected light to
detect a plurality of peaks associated with a plurality of beats of the
user's heart, determining an RR interval based on the plurality of peaks
detected by the peak detection algorithm, determining a confidence level
associated with the RR interval, and based on the confidence level
associated with the RR interval, selecting either the peak detection
algorithm or a frequency analysis algorithm to process data corresponding
to the reflected light to determine the sequence of instantaneous heart
rates of the user.

[0011] In accordance with another exemplary embodiment, a wearable
physiological measurement system is provided. The wearable physiological
measurement system includes a wearable strap configured to be couplable
to an appendage of a user. The strap includes one or more light emitters
for emitting light toward the user's skin, one or more light detectors
for receiving light reflected from the user's skin, and an electronic
circuit board comprising a plurality of electronic components configured
for analyzing data corresponding to the reflected light to automatically
determine a heart rate of the user. The plurality of electronic
components of the electronic circuit board are assembled as a multi-chip
module within the strap such that a first set of the components is
provided as a first electronic circuit board and a second set of the
components is provided as a second electronic circuit board, and wherein
one or more electrical connections are provided between the first and
second electronic circuit boards.

[0012] In accordance with another exemplary embodiment, a wearable
physiological measurement system is provided. The wearable physiological
measurement system includes a plurality of light emitters for emitting
light toward the user's skin, a plurality of light detectors for
receiving light reflected from the user's skin, a motion sensor for
detecting a motion of the user, and a processing module configured to
determine a motion status of the user based on data received from the
motion sensor, and based on the motion status of the user, automatically
and selectively activate one or more of the light emitters to determine a
heart rate of the user.

[0013] In accordance with another exemplary embodiment, a
computer-executable method is provided for use in detecting a heart rate
of a user. The method includes determining a motion status of the user
based on data received from a motion sensor, and based on the motion
status of the user, automatically and selectively activating one or more
light emitters to determine a heart rate of the user.

[0014] In accordance with another exemplary embodiment, one or more
non-transitory computer-readable media are provided having encoded
thereon computer-executable instructions for performing a method. The
method includes determining a motion status of the user based on data
received from a motion sensor, and based on the motion status of the
user, automatically and selectively activating one or more light emitters
to determine a heart rate of the user.

[0015] In accordance with another exemplary embodiment, a wearable
physiological measurement system is provided. The wearable physiological
measurement system includes a plurality of light emitters for emitting
light toward the user's skin, a plurality of light detectors for
receiving light reflected from the user's skin, a motion sensor for
detecting a motion of the user, and a processing module configured to
determine a heart rate of the user based on light received by one or more
of the light detectors, wherein the processing module is further
configured to: process one or more signals generated by at least one of
the sensors, and based on the one or more processed signals,
automatically adjust an operational characteristic of one or more of the
light emitters and/or one or more of the light detectors to minimize
consumption of power by the wearable physiological measurement system.

[0016] In accordance with another exemplary embodiment, a
computer-executable method is provided for use in detecting a heart rate
of a user. The method includes processing one or more signals generated
by at least one sensor, and based on the one or more processed signals,
automatically adjusting an operational characteristic of one or more
light emitters and/or one or more light detectors to minimize consumption
of power by a wearable physiological measurement system collecting heart
rate data.

[0017] In accordance with another exemplary embodiment, one or more
non-transitory computer-readable media are provided having encoded
thereon computer-executable instructions for performing a method. The
method includes processing one or more signals generated by at least one
sensor, and based on the one or more processed signals, automatically
adjusting an operational characteristic of one or more light emitters
and/or one or more light detectors to minimize consumption of power by a
wearable physiological measurement system collecting heart rate data.

[0018] In accordance with another exemplary embodiment, a
computer-executable method is provided for determining an indicator of
cardiovascular intensity experienced by a user. The method includes
programmatically receiving, using a computer system, data corresponding
to heart rate of a user during an exercise routine, transforming the
heart rate data to a time series of heart rate reserve data using a
processing module of the computer system, weighting the heart rate
reserve data according to a weighting scheme using the processing module
of the computer system, programmatically generating, using the processing
module of the computer system, an indicator of cardiovascular intensity
based on the weighted heart rate reserve data, and displaying, on a user
interface rendered on a display device of the computer system, the
indicator of cardiovascular intensity.

[0019] In accordance with another exemplary embodiment, one or more
non-transitory computer-readable media are provided having encoded
thereon computer-executable instructions for performing a method. The
method includes programmatically receiving, using a computer system, data
corresponding to heart rate of a user during an exercise routine,
transforming the heart rate data to a time series of heart rate reserve
data using a processing module of the computer system, weighting the
heart rate reserve data according to a weighting scheme using the
processing module of the computer system, programmatically generating,
using the processing module of the computer system, an indicator of
cardiovascular intensity based on the weighted heart rate reserve data,
and displaying, on a user interface rendered on a display device of the
computer system, the indicator of cardiovascular intensity.

[0020] In accordance with another exemplary embodiment, a computer system
is provided for determining an indicator of cardiovascular intensity
experienced by a user. The computer system includes a processing module
configured or programmed for programmatically receiving data
corresponding to heart rate of a user during an exercise routine,
transforming the heart rate data to a time series of heart rate reserve
data, weighting the heart rate reserve data according to a weighting
scheme, and programmatically generating an indicator of cardiovascular
intensity based on the weighted heart rate reserve data. The computer
system also includes a display device for rendering a user interface on
which the indicator is displayed.

[0021] In accordance with another exemplary embodiment, a
computer-executable method is provided for determining an indicator of
physical recovery of a user. The method includes programmatically
receiving a heart rate variability of a user using a computer system,
programmatically receiving a resting heart rate of the user using the
computer system, programmatically receiving a sleep quality indicator of
the user using the computer system, programmatically generating, using a
processing module of the computer system, a recovery indicator of
physical recovery of the user based on the heart rate variability, the
resting heart rate and the sleep quality indicator, and displaying, on a
user interface rendered on a visual display device of the computer
system, the recovery indicator.

[0022] In accordance with another exemplary embodiment, one or more
non-transitory computer-readable media are provided having encoded
thereon computer-executable instructions for performing a method. The
method includes programmatically receiving a heart rate variability of a
user using a computer system, programmatically receiving a resting heart
rate of the user using the computer system, programmatically receiving a
sleep quality indicator of the user using the computer system,
programmatically generating, using a processing module of the computer
system, a recovery indicator of physical recovery of the user based on
the heart rate variability, the resting heart rate and the sleep quality
indicator, and displaying, on a user interface rendered on a visual
display device of the computer system, the recovery indicator.

[0023] In accordance with another exemplary embodiment, a computer system
is provided for determining an indicator of physical recovery of a user.
The computer system includes a processing module configured or programmed
for programmatically receiving a heart rate variability of a user,
programmatically receiving a resting heart rate of the user,
programmatically receiving a sleep quality indicator of the user, and
programmatically generating a recovery indicator of physical recovery of
the user based on the heart rate variability, the resting heart rate and
the sleep quality indicator. The computer system also includes a display
device for rendering a user interface on which the indicator is
displayed.

BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The foregoing and other objects, aspects, features and advantages
of exemplary embodiments will be more fully understood from the following
description when read together with the accompanying drawings, in which:

[0025] FIG. 1 illustrates a perspective view of an exemplary embodiment of
a wearable physiological measurement system configured as a bracelet
including a strap and a modular head portion.

[0026] FIGS. 2-4 illustrate various exemplary embodiments of a wearable
physiological measurement system according to aspects disclosed herein.

[0027] FIG. 5 illustrates placement of an exemplary wearable physiological
measurement system configured as a bracelet on a user's wrist.

[0029] FIG. 7A illustrates a sectional side view of an exemplary
physiological measurement system including a strap that is not coupled to
a modular head portion.

[0030] FIG. 7B illustrates a sectional side view of the system of FIG. 7A
in which a modular head portion is removably coupled to the strap.

[0031] FIG. 8A depicts a sectional side view of an exemplary wearable
physiological measurement system including a vertically-configured
multi-chip module.

[0032] FIG. 8B depicts a sectional top view of an exemplary wearable
physiological measurement system including a horizontally-configured
multi-chip module.

[0033] FIG. 9 is a flowchart illustrating an exemplary signal processing
algorithm for generating a sequence of heart rates for every detected
heart beat, the algorithm embodied in computer-executable instructions
stored on one or more non-transitory computer-readable media.

[0034] FIG. 10 is a flowchart illustrating an exemplary method of
determining an intensity score, the method embodied in
computer-executable instructions stored on one or more non-transitory
computer-readable media.

[0035] FIG. 11 is a flowchart illustrating an exemplary method by which a
user may use intensity and recovery scores, the method embodied in
computer-executable instructions stored on one or more non-transitory
computer-readable media.

[0036] FIG. 12 illustrates an exemplary display of an intensity score
index indicated in a circular graphic component with an exemplary current
score of 19.0 indicated.

[0037] FIG. 13 illustrates an exemplary display of a recovery score index
indicated in a circular graphic component with a first threshold of 66%
and a second threshold of 33% indicated.

[0040] FIGS. 19A and 19B illustrate an exemplary user interface rendered
on a visual display device for displaying physiological data associated
with a plurality of users.

[0041] FIG. 20 illustrates a user interface that may be used to
independently select time periods of data for multiple users so that data
from the selected periods may be displayed together.

[0042] FIGS. 21A and 21B illustrate an exemplary user interface viewable
by an administrative user, including a selectable and editable listing of
users (e.g., a trainer's clients) whose health information is available
for display.

[0043] FIG. 22 is a block diagram of an exemplary computing device that
may be used to perform any of the methods provided by exemplary
embodiments.

[0044] FIG. 23 is a block diagram of an exemplary distributed computer
system in which various aspects and functions in accord with the present
invention may be practiced.

[0045] FIG. 24 is a diagram of an exemplary network environment suitable
for a distributed implementation of exemplary embodiments.

[0046] The accompanying drawings are not intended to be drawn to scale.

DETAILED DESCRIPTION

[0047] Exemplary embodiments provide physiological measurement systems,
devices and methods for continuous health and fitness monitoring, and
provide improvements to overcome the drawbacks of conventional heart rate
monitors. One aspect of the present disclosure is directed to providing a
lightweight wearable system with a strap that collects various
physiological data or signals from a wearer. The strap may be used to
position the system on an appendage or extremity of a user, for example,
wrist, ankle, and the like. Exemplary systems are wearable and enable
real-time and continuous monitoring of heart rate without the need for a
chest strap or other bulky equipment which could otherwise cause
discomfort and prevent continuous wearing and use. The system may
determine the user's heart rate without the use of electrocardiography
and without the need for a chest strap. Exemplary systems can thereby be
used in not only assessing general well-being but also in continuous
monitoring of fitness. Exemplary systems also enable monitoring of one or
more physiological parameters in addition to heart rate including, but
not limited to, body temperature, heart rate variability, motion, sleep,
stress, fitness level, recovery level, effect of a workout routine on
health and fitness, caloric expenditure, and the like.

[0048] A health or fitness monitor that includes bulky components may
hinder continuous wear. Existing fitness monitors often include the
functionality of a watch, thereby making the health or fitness monitor
quite bulky and inconvenient for continuous wear. Accordingly, one aspect
of the present invention is directed to providing a wearable health or
fitness system that does not include bulky components, thereby making the
bracelet slimmer, unobtrusive and appropriate for continuous wear. The
ability to continuously wear the bracelet further allows continuous
collection of physiological data, as well as continuous and more reliable
health or fitness monitoring. For example, embodiments of the bracelet
disclosed herein allow users to monitor data at all times, not just
during a fitness session. In some embodiments, the wearable system may or
may not include a display screen for displaying heart rate and other
information. In other embodiments, the wearable system may include one or
more light emitting diodes (LEDs) to provide feedback to a user and
display heart rate selectively. In some embodiments, the wearable system
may include a removable or releasable modular head that may provide
additional features and may display additional information. Such a
modular head can be releasably installed on the wearable system when
additional information display is desired, and removed to improve the
comfort and appearance of the wearable system. In other embodiments, the
head may be integrally formed in the wearable system.

[0049] Exemplary embodiments also include computer-executable instructions
that, when executed, enable automatic interpretation of one or more
physiological parameters to assess the cardiovascular intensity
experienced by a user (embodied in an intensity score or indicator) and
the user's recovery after physical exertion or daily stress given sleep
and other forms of rest (embodied in a recovery score). These indicators
or scores may be stored and displayed in a meaningful format to assist a
user in managing his health and exercise regimen. Exemplary
computer-executable instructions may be provided in a cloud
implementation.

[0050] Exemplary embodiments also provide a vibrant and interactive online
community, in the form of a website, for displaying and sharing
physiological data among users. A user of the website may include an
individual whose health or fitness is being monitored, such as an
individual wearing a wearable system disclosed herein, an athlete, a
sports team member, a personal trainer or a coach. In some embodiments, a
user may pick his/her own trainer from a list to comment on their
performance. Exemplary systems have the ability to stream all
physiological information wirelessly, directly or through a mobile
communication device application, to an online website using data
transfer to a cell phone/computer. The website allows users to monitor
their own fitness results, share information with their teammates and
coaches, compete with other users, and win status. Both the wearable
system and the website allows a user to provide feedback regarding
his/her day, exercise and/or sleep, which enables recovery and
performance ratings.

[0051] In an exemplary technique of data transmission, data collected by a
wearable system may be transmitted directly to a cloud-based data
storage, from which data may be downloaded for display and analysis on a
website. In another exemplary technique of data transmission, data
collected by a wearable system may be transmitted via a mobile
communication device application to a cloud-based data storage, from
which data may be downloaded for display and analysis on a website.

[0052] In some embodiments, the website may be a social networking site.
In some embodiments, the website may be displayed using a mobile website
or a mobile application. In some embodiments, the website may be
configured to communicate data to other websites or applications. In some
embodiments, the website may be configured to provide an interactive user
interface. The website may be configured to display results based on
analysis on physiological data received from one or more devices. The
website may be configured to provide competitive ways to compare one user
to another, and ultimately a more interactive experience for the user.
For example, in some embodiments, instead of merely comparing a user's
physiological data and performance relative to that user's past
performances, the user may be allowed to compete with other users and the
user's performance may be compared to that of other users.

[0054] The term "user" as used herein, refers to any type of animal, human
or non-human, whose physiological information may be monitored using an
exemplary wearable physiological monitoring system.

[0055] The term "body," as used herein, refers to the body of a user.

[0056] The term "continuous," as used herein in connection with heart rate
data collection, refers to collection of heart rate data at a sufficient
frequency to enable detection of every heart beat and also refers to
collection of heart rate data continuously throughout the day and night.

[0057] The term "pointing device," as used herein, refers to any suitable
input interface, specifically, a human interface device, that allows a
user to input spatial data to a computing system or device. In an
exemplary embodiment, the pointing device may allow a user to provide
input to the computer using physical gestures, for example, pointing,
clicking, dragging, dropping. Exemplary pointing devices may include, but
are not limited to, a mouse, a touchpad, a touchscreen, and the like.

[0058] The term "multi-chip module," as used herein, refers to an
electronic package in which multiple integrated circuits (IC) are
packaged with a unifying substrate, facilitating their use as a single
component, i.e., as a higher processing capacity IC packaged in a much
smaller volume.

[0059] The term "computer-readable medium," as used herein, refers to a
non-transitory storage hardware, non-transitory storage device or
non-transitory computer system memory that may be accessed by a
controller, a microcontroller, a computational system or a module of a
computational system to encode thereon computer-executable instructions
or software programs. The "computer-readable medium" may be accessed by a
computational system or a module of a computational system to retrieve
and/or execute the computer-executable instructions or software programs
encoded on the medium. The non-transitory computer-readable media may
include, but are not limited to, one or more types of hardware memory,
non-transitory tangible media (for example, one or more magnetic storage
disks, one or more optical disks, one or more USB flash drives), computer
system memory or random access memory, such as, dynamic random-access
memory (DRAM), static random-access memory (SRAM), extended data output
random-access memory (EDO RAM), and the like.

[0060] The term "distal," as used herein, refers to a portion, end or
component of a physiological measurement system that is farthest from a
user's body when worn by the user.

[0061] The term "proximal," as used herein, refers to a portion, end or
component of a physiological measurement system that is closest to a
user's body when worn by the user.

[0062] The term "equal," as used herein, refers, in a broad lay sense, to
exact equality or approximate equality within some tolerance.

II. EXEMPLARY WEARABLE PHYSIOLOGICAL MEASUREMENT SYSTEMS

[0063] Exemplary embodiments provide wearable physiological measurements
systems that are configured to provide continuous measurement of heart
rate. Exemplary systems are configured to be continuously wearable on an
appendage, for example, wrist or ankle, and do not rely on
electrocardiography or chest straps in detection of heart rate. The
exemplary system includes one or more light emitters for emitting light
at one or more desired frequencies toward the user's skin, and one or
more light detectors for received light reflected from the user's skin.
The light detectors may include a photo-resistor, a photo-transistor, a
photo-diode, and the like. As light from the light emitters (for example,
green light) pierces through the skin of the user, the blood's natural
absorbance or transmittance for the light provides fluctuations in the
photo-resistor readouts. These waves have the same frequency as the
user's pulse since increased absorbance or transmittance occurs only when
the blood flow has increased after a heartbeat. The system includes a
processing module implemented in software, hardware or a combination
thereof for processing the optical data received at the light detectors
and continuously determining the heart rate based on the optical data.
The optical data may be combined with data from one or more motion
sensors, e.g., accelerometers and/or gyroscopes, to minimize or eliminate
noise in the heart rate signal caused by motion or other artifacts.

[0064] FIG. 1 illustrates front and back perspective views of one
embodiment of a wearable system configured as a bracelet 100 including
one or more straps 102. FIGS. 2 and 3 show various exemplary embodiments
of a bracelet according to aspects disclosed herein. FIG. 4 illustrates
an exemplary user interface of a bracelet. The bracelet is sleek and
lightweight, thereby making it appropriate for continuous wear. The
bracelet may or may not include a display screen, e.g., a screen 106 such
as a light emitting diode (LED) display for displaying any desired data
(e.g., instantaneous heart rate), as shown and described below with
reference to the exemplary embodiments in FIGS. 2-4.

[0065] As shown in the non-limiting embodiment in FIG. 1, the strap 102 of
the bracelet may have a wider side and a narrower side. In one
embodiment, a user may simply insert the narrower side into the thicker
side and squeeze the two together until the strap is tight around the
wrist, as shown in FIG. 5. To remove the strap, a user may push the strap
further inwards, which unlocks the strap and allows it to be released
from the wrist. In other embodiments, various other fastening means may
be provided. In some embodiments, the strap of the bracelet may be a slim
elastic band formed of any suitable elastic material, for example,
rubber. Certain embodiments of the wearable system may be configured to
have one size that fits all. Other embodiments may provide the ability to
adjust for different wrist sizes.

[0066] As shown in FIG. 1, the wearable system may include components
configured to provide various functions such as data collection and
streaming functions of the bracelet. In some embodiments, the wearable
system may include a button underneath the wearable system. In some
embodiments, the button may be configured such that, when the wearable
system is properly tightened to one's wrist as shown in FIG. 3A, the
button may press down and activate the bracelet to begin storing
information. In other embodiments, the button may be disposed and
configured such that it may be pressed manually at the discretion of a
user to begin storing information or otherwise to mark the start or end
of an activity period. In some embodiments, the button may be held to
initiate a time-stamp and held again to end a time-stamp, which may be
transmitted, directly or through a mobile communication device
application, to a website as a time-stamp. Time-stamp information may be
used, for example, as a privacy setting to indicate periods of activity
during which physiological data may not be shared with other users. In
some embodiments, the wearable system may be waterproof so that users
never need to remove it, thereby allowing for continuous wear.

[0067] The wearable system includes a heart rate monitor. In one example,
the heart rate may be detected from the radial artery, in the exemplary
positioning shown in FIG. 5. See, Certified Nursing Association, "Regular
monitoring of your patient's radial pulse can help you detect changes in
their condition and assist in providing potentially life-saving care."
See, http://cnatraininghelp.com/cna-skills/counting-and-recording-a-radia-
l-pulse, the entire contents of which are incorporated herein by
reference. Thus, the wearable system may include a pulse sensor. In one
embodiment, the wearable system may be configured such that, when a user
wears it around their wrist and tightens it, the sensor portion of the
wearable system is secured over the user's radial artery or other blood
vessel. Secure connection and placement of the pulse sensor over the
radial artery or other blood vessel allow measurement of heart rate and
pulse.

[0068] In some embodiments, the pulse or heart rate may be taken using an
optical sensor coupled with one or more light emitting diodes (LEDs), all
directly in contact with the user's wrist. The LEDs are provided in a
suitable position from which light can be emitted into the user's skin.
In one example, the LEDs mounted on a side or top surface of a circuit
board in the system to prevent heat buildup on the LEDs and to prevent
burns on the skin. Cleverly designed elastic wrist straps can ensure that
the sensors are always in contact with the skin and that there is a
minimal amount of outside light seeping into the sensors.

[0069] In some embodiments, the wearable system may be configured to
record other physiological parameters including, but not limited to, skin
temperature (using a thermometer), galvanic skin response (using a
galvanic skin response sensor), motion (using one or more multi-axes
accelerometers and/or gyroscope), and the like, and environmental or
contextual parameters, e.g., ambient temperature, humidity, time of day,
and the like.

[0070] In some embodiments, the wearable system may further be configured
such that a button underneath the system may be pressed against the
user's wrist, thus triggering the system to begin one or more of
collecting data, calculating metrics and communicating the information to
a network. In some embodiments, the same sensor used for measuring heart
rate may be used to indicate whether the user is wearing the wearable
system or not. In some embodiments, power to the one or more LEDs may be
cut off as soon as this situation is detected, and reset once the user
has put the wearable system back on their wrist.

[0071] The wearable system may include one, two or more sources of battery
life. In some embodiments, it may have a battery that can slip in and out
of the head of the wearable system and can be recharged using an included
accessory. Additionally, the wearable system may have a built-in battery
that is less powerful. When the more powerful battery is being charged,
the user does not need to remove the wearable system and can still record
data (during sleep, for example).

[0072] In some embodiments, the an application associated with data from
an exemplary wearable system (e.g., a mobile communication device
application) may include a user input component for enabling the user to
indicate his/her feelings. When the data is uploaded from the wearable
system directly or indirectly to a website, the website may record a
user's "Vibes" alongside their duration of exercise and sleep.

[0073] In exemplary embodiments, the wearable system is enabled to
automatically detect when the user is asleep, awake but at rest and
exercising based on physiological data collected by the system.

[0074] As shown in the exemplary embodiment of FIG. 4, a rotatable wheel
108 may be provided at the center of the wearable system to control
whether the system is displaying the heart rate. For example, when the
wheel is turned to the right however, the system continuously shows heart
rate, and turns off the heart rate display when the wheel is turned to
the right again. In one example, turning the wheel to the right and
holding it there creates a time-stamp to indicate the duration of
exercise. Turning the wheel to the left and holding it there forces data
transmission to a cell phone, external computer or the Internet. In other
embodiments, the wheel 108 may be absent in the wearable system. In some
embodiments, the functionality of a rotatable wheel described herein may
be provided in an application of a mobile communication device that is
associated with physiological data collected from a wearable system.

[0075] FIG. 6 shows a block diagram illustrating exemplary components of a
wearable physiological measurement system 600 configured to provide
continuous collection and monitoring of physiological data. The wearable
system 600 includes one or more sensors 602. As discussed above, the
sensors 602 may include a heart rate monitor. In some embodiments, the
wearable system 600 may further include one or more of sensors for
detecting calorie burn, distance and activity. Calorie burn may be based
on a user's heart rate, and a calorie burn measurement may be improved if
a user chooses to provide his or her weight and/or other physical
parameters. In some embodiments, manual entering of data is not required
in order to derive calorie burn; however, data entry may be used to
improve the accuracy of the results. In some embodiments, if a user has
forgotten to enter a new weight, he/she can enter it for past weeks and
the calorie burn may be updated accordingly.

[0076] The sensors 602 may include one or more sensors for activity
measurement. In some embodiments, the system may include one or more
multi-axes accelerometers and/or gyroscope to provide a measurement of
activity. In some embodiments, the accelerometer may further be used to
filter a signal from the optical sensor for measuring heart rate and to
provide a more accurate measurement of the heart rate. In some
embodiments, the wearable system may include a multi-axis accelerometer
to measure motion and calculate distance, whether it be in real terms as
steps or miles or as a converted number. Activity sensors may be used,
for example, to classify or categorize activity, such as walking,
running, performing another sport, standing, sitting or lying down. In
some embodiments, one or more of collected physiological data may be
aggregated to generate an aggregate activity level. For example, heart
rate, calorie burn, and distance may be used to derive an aggregate
activity level. The aggregate level may be compared with or evaluated
relative to previous recordings of the user's aggregate activity level,
as well as the aggregate activity levels of other users.

[0077] The sensors 602 may include a thermometer for monitoring the user's
body or skin temperature. In one embodiment, the sensors may be used to
recognize sleep based on a temperature drop, lack of activity according
to data collected by the accelerometer, and reduced heart rate as
measured by the heart rate monitor. The body temperature, in conjunction
with heart rate monitoring and motion, may be used to interpret whether a
user is sleeping or just resting, as body temperature drops significantly
when an individual is about to fall asleep), and how well an individual
is sleeping as motion indicates a lower quality of sleep. The body
temperature may also be used to determine whether the user is exercising
and to categorize and/or analyze activities.

[0078] The system 600 includes one or more batteries 604. According to one
embodiment, the one or more batteries may be configured to allow
continuous wear and usage of the wearable system. In one embodiment, the
wearable system may include two or more batteries. The system may include
a removable battery that may be recharged using a charger. In one
example, the removable battery may be configured to slip in and out of a
head portion of the system. In one example, the removable battery may be
able to power the system for around a week. Additionally, the system may
include a built-in battery. The built-in battery may be recharged by the
removable battery. The built-in battery may be configured to power the
bracelet for around a day on its own. When the more removable battery is
being charged, the user does not need to remove the system and may
continue collecting data using the built-in battery. In other
embodiments, the two batteries may both be removable and rechargeable.

[0079] In some embodiments, the system 600 may include a battery that is a
wireless rechargeable battery. For example, the battery may be recharged
by placing the system or the battery on a rechargeable mat. In other
example, the battery may be a long range wireless rechargeable battery.
In other embodiments, the battery may be a rechargeable via motion. In
yet other embodiments, the battery may be rechargeable using a solar
energy source.

[0080] The wearable system 600 includes one or more non-transitory
computer-readable media 606 for storing raw data detected by the sensors
of the system and processed data calculated by a processing module of the
system.

[0081] The system 600 includes a processor 608, a memory 610, a bus 612, a
network interface 614 and an interface 616. The network interface 614 is
configured to wirelessly communicate data to an external network. Some
embodiments of the wearable system may be configured to stream
information wirelessly to a social network. In some embodiments, data
streamed from a user's wearable system to an external network may be
accessed by the user via a website. The network interface may be
configured such that data collected by the system may be streamed
wirelessly. In some embodiments, data may be transmitted automatically,
without the need to manually press any buttons. In some embodiments, the
system may include a cellular chip built into the system. In one example,
the network interface may be configured to stream data using Bluetooth
technology. In another example, the network interface may be configured
to stream data using a cellular data service, such as via a 3G or 4G
cellular network.

[0082] In some embodiments, a physiological measurement system may be
configured in a modular design to enable continuous operation of the
system in monitoring physiological information of a user wearing the
system. The module design may include a strap and a separate modular head
portion or housing that is removably couplable to the strap. FIG. 7A
illustrates a side view of an exemplary physiological measurement system
100 including a strap 102 that is not coupled to a modular head portion
or housing 104. FIG. 7B illustrates a side view of the system 100 in
which the modular head portion 104 is removably coupled to the strap 102.

[0083] In the non-limiting illustrative module design, the strap 102 of a
physiological measurement system may be provided with a set of components
that enables continuous monitoring of at least a heart rate of the user
so that it is independent and fully self-sufficient in continuously
monitoring the heart rate without requiring the modular head portion 104.
In one embodiment, the strap includes a plurality of light emitters for
emitting light toward the user's skin, a plurality of light detectors for
receiving light reflected from the user's skin, an electronic circuit
board comprising a plurality of electronic components configured for
analyzing data corresponding to the reflected light to automatically and
continually determine a heart rate of the user, and a first set of one or
more batteries for supplying electrical power to the light emitters, the
light detectors and the electronic circuit board. In some embodiments,
the strap may also detect one or more other physiological characteristics
of the user including, but not limited to, temperature, galvanic skin
response, and the like. The strap may include one or more slots for
permanently or removably coupling batteries 702 to the strap 102.

[0084] The strap 102 may include an attachment mechanism 706, e.g., a
press-fit mechanism, for coupling the modular head portion 104 to the
strap 102. The modular head portion 104 may be coupled to the strap 102
at any desired time by the user to impart additional functionality to the
system 100. In one embodiment, the modular head portion 104 includes a
second set of one or more batteries 704 chargeable by an external power
source so that the second set of batteries can be used to charge or
recharge the first set of batteries 702 in the strap 102. The combination
of the first and second sets of batteries enables the user to
continuously monitor his/her physiological information without having to
remove the strap for recharging. In some embodiments, the module head
portion may include one or more additional components including, but not
limited to, an interface 616 including visual display device configured
to render a user interface for displaying physiological information of
the user, a global positioning system (GPS) sensor, an electronic circuit
board (e.g., to process GPS signals), and the like.

[0085] Certain exemplary systems may be configured to be coupled to any
desired part of a user's body so that the system may be moved from one
portion of the body (e.g., wrist) to another portion of the body (e.g.,
ankle) without affecting its function and operation. An exemplary system
may include an electronic circuit board comprising a plurality of
electronic components configured for analyzing data corresponding to the
reflected light to automatically and continually determine a heart rate
of the user. The electronic circuit board implements a processing module
configured to detect an identity of a portion of the user's body, for
example, an appendage like wrist, ankle, to which the strap is coupled
based on one or more signals associated with the heart rate of the user,
and, based on the identity of the appendage, adjust data analysis of the
reflected light to determine the heart rate of the user.

[0086] In one embodiment, the identity of the portion of the user's body
to which the wearable system is attached may be determined based on one
or more parameters including, but not limited to, absorbance level of
light as returned from the user's skin, reflectance level of light as
returned from the user's skin, motion sensor data (e.g., accelerometer
and/or gyroscope), altitude of the wearable system, and the like.

[0087] In some embodiments, the processing module is configured to
determine that the wearable system is taken off from the user's body. In
one example, the processing module may determine that the wearable system
has been taken off if data from the galvanic skin response sensor
indicates data atypical of a user's skin. If the wearable system is
determined to be taken off from the user's body, the processing module is
configured to deactivate the light emitters and the light detectors and
cease monitoring of the heart rate of the user to conserve power.

[0088] In some exemplary embodiments, the electronic components of the
physiological measurement system may be provided in the form of a
multi-chip module in which a plurality of electrically-coupled electronic
circuit boards are provided separately within the system. In one
non-limiting example, the processor and random-access memory (RAM) may be
provided on a first circuit board, wireless communication components may
be provided on a second circuit board, and sensors may be provided on a
third circuit board. The separate electronic circuit boards may be
provided in a modular head of the system and/or along a strap of the
system. The term "multi-chip module," as used herein, refers to an
electronic package in which multiple integrated circuits (IC) are
packaged with a unifying substrate, facilitating their use as a single
component, i.e., as a higher processing capacity IC packaged in a much
smaller volume. Each IC can comprise a circuit fabricated in a thinned
semiconductor wafer. Any suitable set of one or more electronic
components may be provided in the circuit boards of a multi-chip module.
Exemplary embodiments also provide methods for fabricating and assembling
multi-chip modules as taught herein.

[0089] Exemplary numbers of chips integrated in a multi-chip module may
include, but are not limited to, two, three, four, five, six, seven,
eight, and the like. In one embodiment of a physiological measurement
system, a single multi-chip module is provided on a circuit board that
performs operations to generate physiological information associated with
a user of the system. In other embodiments, a plurality of multi-chip
modules are provided on a circuit board of the physiological measurement
system. The plurality of multi-chip modules may be stacked vertically on
top of one another on the circuit board to further minimize the packaging
size and the footprint of the circuit board.

[0090] In one multi-chip embodiment, two or more electrically-coupled
circuit boards of a multi-chip module may be provided in a physiological
measurement system in a vertically stacked manner to minimize the
packaging size and the footprint of the circuit board. Vertically
stacking the components on a circuit board minimizes the packaging size
(e.g., the length and width) and the footprint occupied by the chips on
the circuit board. In certain non-limiting embodiments, a circuit board
including one or more physiological sensors may be placed closest to,
proximal to or in contact with the user's skin, while one or more circuit
boards including one or more processors, storage devices, communication
components and non-physiological sensors may be provided in vertical
layers that are distal to the user's skin.

[0091] FIGS. 8A and 8B depict a schematic side view and top view,
respectively, of an exemplary physiological measurement system 100
including a head portion 104, a strap 102 and a multi-chip module. The
head portion and/or the strap may include a circuit board 802 including a
multi-chip module assembled in a vertically stacked configuration. Two or
more layers of active electronic integrated circuit (IC) components are
integrated vertically into a single circuit in the circuit board. The IC
layers are oriented in spaced planes that extend substantially parallel
to one another in a vertically stacked configuration. As illustrated in
FIG. 8A, the circuit board 802 includes a substrate 804 for supporting
the multi-chip module. A first integrated circuit chip 806 is coupled to
the substrate 804 using any suitable coupling mechanism, for example,
epoxy application and curing. A first spacer layer 808 is coupled to the
surface of the first integrated circuit chip 806 opposite to the
substrate 804 using, for example, epoxy application and curing. A second
integrated circuit chip 810 is coupled to the surface of the first spacer
layer 808 opposite to the first integrated circuit chip 806 using, for
example, epoxy application and curing. The first and second integrated
circuit chips 806 and 810 are electrically coupled using wiring 812.

[0092] In some embodiments, a metal frame may be provided for mechanical
and/or electrical connection among the integrated circuit chips. An
exemplary metal frame may take the form of a leadframe. The first and
second integrated circuit chips may be coupled to the metal frame using
wiring. A packaging may be provided to encapsulate the multi-chip module
assembly and to maintain the multiple integrated circuit chips in
substantially parallel arrangement with respect to one another.

[0093] As illustrated in FIG. 8A, the vertical three-dimensional stacking
of the first integrated circuit chip 806 and the second integrated
circuit chip 810 provides high-density functionality on the circuit board
while minimizing overall packaging size and footprint (as compared to a
circuit board that does not employ a vertically stacked multi-chip
module). One of ordinary skill in the art will recognize that an
exemplary multi-chip module is not limited to two stacked integrated
circuit chips. Exemplary numbers of chips vertically integrated in a
multi-chip module may include, but are not limited to, two, three, four,
five, six, seven, eight, and the like.

[0094] In one embodiment, a single multi-chip module is provided. In other
embodiments, a plurality of multi-chip modules as illustrated in FIG. 8A
is provided. In an exemplary embodiment, a plurality of multi-chip
modules (for example, two multi-chip modules) may be stacked vertically
on top of one another on a circuit board of a physiological measurement
system to further minimize the packaging size and footprint of the
circuit board.

[0095] In addition to the need for reducing the footprint, there is also a
need for decreasing the overall package height in multi-chip modules.
Exemplary embodiments may employ wafer thinning to sub-hundreds micron to
reduce the package height in multi-chip modules. Any suitable technique
can be used to assemble a multi-chip module on a substrate. Exemplary
assembly techniques include, but are not limited to, laminated MCM
(MCM-L) in which the substrate is a multi-layer laminated printed circuit
board, deposited MCM (MCM-D) in which the multi-chip modules are
deposited on the base substrate using thin film technology, and ceramic
substrate MCM (MCM-C) in which several conductive layers are deposited on
a ceramic substrate and embedded in glass layers that layers are co-fired
at high temperatures (HTCC) or low temperatures (LTCC).

[0096] In another multi-chip embodiment illustrated in FIG. 8B, two or
more electrically-coupled circuit boards of a multi-chip module may be
provided in a physiological measurement system in a horizontally spaced
manner to minimize the height of the circuit board. Providing the
components on a circuit board in a horizontally spaced manner minimizes
the packaging height occupied by the chips on the circuit board. In
certain non-limiting embodiments, a circuit board including one or more
physiological sensors may be placed close to or in contact with the
user's skin so that physiological signals are detected reliably, while
one or more circuit boards including one or more processors, storage
devices, communication components and non-physiological sensors may be
provided may be distributed throughout the wearable system to provide
improved flexibility, wearability, comfort and durability of the system.

[0097] FIG. 8B depicts a schematic top view of an exemplary physiological
measurement system 100 including a head portion 104 and a strap 102. The
head portion 104 and/or the strap 102 may include a circuit board
including a plurality of integrated circuit boards or chips 820, 822, 824
forming a multi-chip module assembled in a horizontally spaced
configuration. The integrated circuit chips are electrically coupled to
one another using wiring 826. The circuit chips may be distributed
through the head portion and/or the strap of the system. In the
non-limiting illustrative embodiment, for example, one chip is provided
in the head portion and two chips are provided in the strap.

[0098] Exemplary systems include a processing module configured to filter
the raw photoplethysmography data received from the light detectors to
minimize contributions due to motion, and subsequently process the
filtered data to detect peaks in the data that correspond with heart
beats of a user. The overall algorithm for detecting heart beats takes as
input the analog signals from optical sensors (mV) and accelerometer, and
outputs an implied beats per minute (heart rate) of the signal accurate
within a few beats per minute as that determined by an
electrocardiography machine even during motion.

[0099] FIG. 9 is a flowchart illustrating an exemplary signal processing
algorithm for generating a sequence of heart rates for every detected
heart beat, that is embodied in computer-executable instructions stored
on one or more non-transitory computer-readable media. In step 902, light
emitters of a wearable physiological measurement system emit light toward
a user's skin. In step 904, light reflected from the user's skin is
detected at the light detectors in the system. In step 906, signals or
data associated with the reflected light are pre-processed using any
suitable technique to facilitate detection of heart beats. In step 908, a
processing module of the system executes one or more computer-executable
instructions associated with a peak detection algorithm to process data
corresponding to the reflected light to detect a plurality of peaks
associated with a plurality of beats of the user's heart. In step 910,
the processing module determines an R-wave-to-R-wave interval (RR
interval) based on the plurality of peaks detected by the peak detection
algorithm. In step 912, the processing module determines a confidence
level associated with the RR interval estimate.

[0100] Based on the confidence level associated with the RR interval
estimate, the processing module selects either the peak detection
algorithm or a frequency analysis algorithm to process data corresponding
to the reflected light to determine the sequence of instantaneous heart
rates of the user. The frequency analysis algorithm may process the data
corresponding to the reflected light based on the motion of the user
detected using, for example, an accelerometer and/or a gyroscope. The
processing module may select the peak detection algorithm or the
frequency analysis algorithm regardless of a motion status of the user.
It is advantageous to use the confidence in the estimate in deciding
whether to switch to frequency-based methods as certain frequency-based
approaches are unable to obtain accurate RR intervals for heart rate
variability analysis. Therefore, the present invention maintains the
ability to obtain the RR intervals for as long as possible, even in the
case of motion, thereby maximizing the information that can be extracted.

[0101] For example, in step 914, it is determined whether the confidence
level associated with the RR interval estimate is above (or equal to or
above) a threshold. In certain embodiments, the threshold may be
predefined, for example, about 50%-90% in some embodiments and about 80%
in one non-limiting embodiment. In other embodiments, the threshold may
be adaptive, i.e., the threshold may be dynamically and automatically
determined based on previous confidence levels. For example, if one or
more previous confidence levels were high (i.e., above a certain level),
the system may determine that a present confidence level that is
relatively low compared to the previous levels is indicative of a less
reliable signal. In this case, the threshold may be dynamically adjusted
to be higher so that a frequency-based analysis method may be selected to
process the less reliable signal.

[0102] If the confidence level is above (or equal to or above) the
threshold, in step 916, the processing module may use the plurality of
peaks to determine an instantaneous heart rate of the user. On the other
hand, in step 920, based on a determination that the confidence level
associated with the RR interval is below (or equal to or below) the
threshold, the processing module may execute one or more
computer-executable instructions associated with the frequency analysis
algorithm to determine an instantaneous heart rate of the user.

[0103] In some embodiments, in steps 918 or 922, the processing module
determines a heart rate variability of the user based on the sequence of
the instantaneous heart rates.

[0104] The wearable system may include or be coupled to (in a wired manner
or wirelessly) a display device configured to render a user interface for
displaying the sequence of the instantaneous heart rates of the user, the
RR intervals and/or the heart rate variability determined by the
processing module. The system may include or be coupled to a storage
device configured to store the sequence of the instantaneous heart rates,
the RR intervals and/or the heart rate variability determined by the
processing module.

[0105] An exemplary peak detection algorithm uses a probabilistic peak
detection algorithm. A discrete probabilistic step is set. The likelihood
function is a mixture of a Gaussian random variable and a uniform. The
heart of the likelihood function encodes the assumption that with
probability (p) the peak detection algorithm has produced a reasonable
initial estimate, but with probability (1-p) it has not. In a subsequent
step, Bayes' rule is applied to determine the posterior density on the
parameter space, of which the maximum is taken, i.e., the argument
(parameter) that maximizes the posterior distribution. This value is the
estimate for the heart rate. In a subsequent step, the previous two steps
are reapplied for the rest of the sample. There is some variance in the
signal due to process noise, which is dependent on the length of the
interval. This process noise becomes the variance in the Gaussians used
for the likelihood function. Then, the estimate is obtained as the
maximum a posteriori on the new posterior distribution. A confidence
value is recorded for the estimate for which, for some precision
measurement, the posterior value is summed at points in the parameter
space centered at the estimate plus or minus the precision.

[0106] The beats per minute (BPM) parameter space, θ, may range
between about 20 and about 240, corresponding to the empirical bounds on
human heart rates. In an exemplary method, a probability distribution is
calculated over this parameter space, at each step declaring the mode of
the distribution to be the heart rate estimate. A discrete uniform prior
may be set:

π1˜DiscUnif(θ)

[0107] The un-normalized, univariate likelihood is defined by a mixture of
a Gaussian function and a uniform:

l1˜IG+(1-I)U,
G˜N(γ1σ2),I˜Ber(p)

[0108] where

U˜DiscUnif(θ)

[0109] and where σ and p are predetermined constants.

[0110] Bayes' rule is applied to determine the posterior density on
θ, for example, by component-wise multiplying the prior density
vector (π1(θ)).sub.θεθ with the
likelihood vector (l1(θ)).sub.θεθ to
obtain the posterior distribution η1. Then, the following is
set:

β1=argmax.sub.θεθη1(θ)

[0111] For k≧2, the variance in signal S(t) due to process noise is
determined. Then, the following variable is set to imbue temporally long
RR intervals with more process/interpeak noise and set the
post-normalization convolution:

πk=ηk-1*fN(o,λk2.sub.)|θ

[0112] where f is a density function of the following:

Z˜N(o,λk2)

[0113] Then, the following expressions are calculated:

lk˜pGk+(1-p)U, Gk˜N(λk,σ2)

[0114] The expression is then normalized and recorded:

βk=argmax.sub.θεθηk(θ)

[0115] Finally, the confidence level of the above expression for a
particular precision threshold is determined:

C k = θ ε [ β k - e 1 ,
β k + e ] θ η k . ##EQU00001##

[0116] An exemplary frequency analysis algorithm used in the present
invention isolates the highest frequency components of the optical heart
rate data, checks for harmonics common in both the accelerometer data and
the optical data, and performs filtering of the optical data. The
algorithm takes as input raw analog signals from the accelerometer
(3-axis) and pulse sensors, and outputs heart rate values or beats per
minute (BPM) for a given period of time related to the window of the
spectrogram.

[0117] The isolation of the highest frequency components is performed in a
plurality of stages, gradually winnowing the window-sizes of
consideration, thereby narrowing the range of errors. In one
implementation, a spectrogram of 2 15 samples with overlap 2 13 samples
of the optical data is generated. The spectrogram is restricted to
frequencies in which heart rate can lie. These restriction boundaries may
be updated when smaller window sizes are considered. The frequency
estimate is extracted from the spectrogram by identifying the most
prominent frequency component of the spectrogram for the optical data.
The frequency may be extracted using the following exemplary steps. The
most prominent frequency of the spectrogram is identified in the signal.
It is determined if the frequency estimate is a harmonic of the true
frequency. The frequency estimate is replaced with the true frequency if
the estimate is a harmonic of the true frequency. It is determined if the
current frequency estimate is a harmonic of the motion sensor data. The
frequency estimate is replaced with a previous temporal estimate if it is
a harmonic of the motion sensor data. The upper and lower bounds on the
frequency obtained are saved. A constant value may be added or subtracted
in some cases. In subsequent steps, the constant added or subtracted may
be reduced to provide narrower searches. A number of the previous steps
are repeated one or more times, e.g., three times, except taking 2 {15-i}
samples for the window size and 2 {13-i} for the overlap in the
spectrogram where i is the current number of iteration. The final output
is the average of the final symmetric endpoints of the frequency
estimation.

[0118] The table below demonstrates the performance of the algorithm
disclosed herein. To arrive at the results below, experiments were
conducted in which a subject wore an exemplary wearable physiological
measurement system and a 3-lead electrocardiography (ECG) system, which
were both wired to the same microcontroller (e.g., Arduino) in order to
provide time-synced data. Approximately 50 data sets were analyzed which
included the subject standing still, walking, and running on a treadmill.

[0119] The algorithm's performance comes from a combination of a
probabilistic and frequency based approach. The three difficulties in
creating algorithms for heart rate calculations from the PPG data are 1)
false detections of beats, 2) missed detections of real beats, and 3)
errors in the precise timing of the beat detection. The algorithm
disclosed herein provides improvements in these three sources of error
and, in some cases, the error is bound to within 2 BPM of ECG values at
all times even during the most motion-intense activities.

[0120] The exemplary wearable system computes heart rate variability (HRV)
to obtain an understanding of the recovery status of the body. These
values are captured right before a user awakes or when the user is not
moving, in both cases photoplethysmography (PPG) variability yielding
equivalence to the ECG HRV. HRV is traditionally measured using an ECG
machine and by obtaining a time series of R-R intervals. Because an
exemplary wearable system utilizes photoplethysmography (PPG), it does
not obtain the electric signature from the heart beats; instead, the
peaks in the obtained signal correspond to arterial blood volume. At
rest, these peaks are directly correlated with cardiac cycles which
enables the calculation of HRV via analyzing peak-to-peak intervals (the
PPG analog of RR intervals). It has been demonstrated in the medical
literature that these peak-to-peak intervals, the "PPG variability," is
identical to ECG HRV while at rest. See, Charlot K, et. al.
"Interchangeability between heart rate and photoplehysmography
variabilities during sympathetic stimulations." Physiological
Measurement. 2009 December; 30(12): 1357-69. doi:
10.1088/0967-3334/30/12/005. URL:
http://www.ncbi.nlm.nih.gov/pubmed/19864707; and Lu, S, et. al. "Can
photoplethysmography variability serve as an alternative approach to
obtain heart rate variability information?" Journal of Clinical
Monitoring and Computing. 2008 February; 22(1):23-9. URL:
http://www.ncbi.nlm.nih.gov/pubmed/17987395, the entire contents of which
are incorporated herein by reference.

[0121] Exemplary physiological measurement systems are configured to
minimize power consumption so that the systems may be worn continuously
without requiring power recharging at frequent intervals. The majority of
current draw in an exemplary system is allocated to power the light
emitters, e.g., LEDs, the wireless transceiver, the microcontroller and
peripherals. In one embodiment, the circuit board of the system may
include a boost converter that runs a current of about 10 mA through each
of the light emitters with an efficiency of about 80% and may draw power
directly from the batteries at substantially constant power. With
exemplary batteries at about 3.7 V, the current draw from the battery may
be about 40 mW. In some embodiments, the wireless transceiver may draw
about 10-20 mA of current when it is actively transferring data. In some
embodiments, the microcontroller and peripherals may draw about 5 mA of
current.

[0122] An exemplary system may include a processing module that is
configured to automatically adjust one or more operational
characteristics of the light emitters and/or the light detectors to
minimize power consumption while ensuring that all heart beats of the
user are reliably and continuously detected. The operational
characteristics may include, but are not limited to, a frequency of light
emitted by the light emitters, the number of light emitters activated, a
duty cycle of the light emitters, a brightness of the light emitters, a
sampling rate of the light detectors, and the like.

[0123] The processing module may adjust the operational characteristics
based on one or more signals or indicators obtained or derived from one
or more sensors in the system including, but not limited to, a motion
status of the user, a sleep status of the user, historical information on
the user's physiological and/or habits, an environmental or contextual
condition (e.g., ambient light conditions), a physical characteristic of
the user (e.g., the optical characteristics of the user's skin), and the
like.

[0124] In one embodiment, the processing module may receive data on the
motion of the user using, for example, an accelerometer. The processing
module may process the motion data to determine a motion status of the
user which indicates the level of motion of the user, for example,
exercise, light motion (e.g., walking), no motion or rest, sleep, and the
like. The processing module may adjust the duty cycle of one or more
light emitters and the corresponding sampling rate of the one or more
light detectors based on the motion status. For example, upon determining
that the motion status indicates that the user is at a first higher level
of motion, the processing module may activate the light emitters at a
first higher duty cycle and sample the reflected light using light
detectors sampling at a first higher sampling rate. Upon determining that
the motion status indicates that the user is at a second lower level of
motion, the processing module may activate the light emitters at a second
lower duty cycle and sample the reflected light using light detectors
sampling at a second lower sampling rate. That is, the duty cycle of the
light emitters and the corresponding sampling rate of the light detectors
may be adjusted in a graduated or continuous manner based on the motion
status or level of motion of the user. This adjustment ensures that heart
rate data is detected at a sufficiently high frequency during motion to
reliably detect all of the heart beats of the user.

[0125] In non-limiting examples, the light emitters may be activated at a
duty cycle ranging from about 1% to about 100%. In another example, the
light emitters may be activated at a duty cycle ranging from about 20% to
about 50% to minimize power consumption. Certain exemplary sampling rates
of the light detectors may range from about 50 Hz to about 1000 Hz, but
are not limited to these exemplary rates. Certain non-limiting sampling
rates are, for example, about 100 Hz, 200 Hz, 500 Hz, and the like.

[0126] In one non-limiting example, the light detectors may sample
continuously when the user is performing an exercise routine so that the
error standard deviation is kept within 5 beats per minute (BPM). When
the user is at rest, the light detectors may be activated for about a 1%
duty cycle--10 milliseconds each second (i.e., 1% of the time) so that
the error standard deviation is kept within 5 BPM (including an error
standard deviation in the heart rate measurement of 2 BPM and an error
standard deviation in the heart rate changes between measurement of 3
BPM). When the user is in light motion (e.g., walking), the light
detectors may be activated for about a 10% duty cycle--100 milliseconds
each second (i.e., 10% of the time) so that the error standard deviation
is kept within 6 BPM (including an error standard deviation in the heart
rate measurement of 2 BPM and an error standard deviation in the heart
rate changes between measurement of 4 BPM).

[0127] The processing module may adjust the brightness of one or more
light emitters by adjusting the current supplied to the light emitters.
For example, a first level of brightness may be set by current ranging
between about 1 mA to about 10 mA, but is not limited to this exemplary
range. A second higher level of brightness may be set by current ranging
from about 11 mA to about 30 mA, but is not limited to this exemplary
range. A third higher level of brightness may be set by current ranging
from about 80 mA to about 120 mA, but is not limited to this exemplary
range. In one non-limiting example, first, second and third levels of
brightness may be set by current of about 5 mA, about 20 mA and about 100
mA, respectively.

[0128] In some embodiments, the processing module may detect an
environmental or contextual condition (e.g., level of ambient light) and
adjust the brightness of the light emitters accordingly to ensure that
the light detectors reliably detect light reflected from the user's skin
while minimizing power consumption. For example, if it is determined that
the ambient light is at a first higher level, the brightness of the light
emitters may be set at a first higher level. If it is determined that the
ambient light is at a second lower level, the brightness of the light
emitters may be set at a second lower level. The brightness may be
adjusted in a graduated or continuous manner based on the detected
environment conditions.

[0129] In some embodiments, the processing module may detect a
physiological condition of the user (e.g., an optical characteristic of
the user's skin) and adjust the brightness of the light emitters
accordingly to ensure that the light detectors reliably detect light
reflected from the user's skin while minimizing power consumption. For
example, if it is determined that the user's skin is highly reflective,
the brightness of the light emitters may be set at a first lower level.
If it is determined that the user's skin is not very reflective, the
brightness of the light emitters may be set at a second higher level.

[0130] Shorter-wavelength LEDs require more power than that required by
longer-wavelength LEDs. Therefore, an exemplary wearable system may
provide and use light emitted at two or more different frequencies based
on the level of motion detected in order to save battery life. For
example, upon determining that the motion status indicates that the user
is at a first higher level of motion (e.g., exercising), one or more
light emitters may be activated to emit light at a first wavelength. Upon
determining that the motion status indicates that the user is at a second
lower level of motion (e.g., at rest), one or more light emitters may be
activated to emit light at a second wavelength that is longer than the
first wavelength. Upon determining that the motion status indicates that
the user is at a third lower level of motion (e.g., sleeping), one or
more light emitters may be activated to emit light at a third wavelength
that is longer than the first and second wavelengths. Other levels of
motion may be predetermined and corresponding wavelengths of emitted
light may be selected. The wavelength may be adjusted in a graduated or
continuous manner. The threshold levels of motion that trigger adjustment
of the light wavelength may be based on one or more factors including,
but are not limited to, skin properties, ambient light conditions, and
the like. Any suitable combination of light wavelengths may be selected,
for example, green (for a higher level of motion)/red (for a lower level
of motion); red (for a higher level of motion)/infrared (for a lower
level of motion); blue (for a higher level of motion)/green (for a lower
level of motion); and the like.

[0131] Shorter-wavelength LEDs require more power than is required by
other types of heart rate sensors, such as, a piezo-sensor or an infrared
sensor. Therefore, an exemplary wearable system may provide and use a
unique combination of sensors--one or more light detectors for periods
where motion is expected and one or more piezo and/or infrared sensors
for low motion periods (e.g., sleep)--to save battery life. Certain other
embodiments of a wearable system may exclude piezo-sensors and/or
infrared sensors.

[0132] For example, upon determining that the motion status indicates that
the user is at a first higher level of motion (e.g., exercising), one or
more light emitters may be activated to emit light at a first wavelength.
Upon determining that the motion status indicates that the user is at a
second lower level of motion (e.g., at rest), non-light based sensors may
be activated. The threshold levels of motion that trigger adjustment of
the type of sensor may be based on one or more factors including, but are
not limited to, skin properties, ambient light conditions, and the like.

[0133] The system may determine the type of sensor to use at a given time
based on the level of motion (e.g., via an accelerometer) and whether the
user is asleep (e.g., based on movement input, skin temperature and heart
rate). Based on a combination of these factors the system selectively
chooses which type of sensor to use in monitoring the heart rate of the
user. Common symptoms of being asleep are periods of no movement or small
bursts of movement (such as shifting in bed), lower skin temperature
(although it is not a dramatic drop from normal), and heart rate that is
below the typical resting heart rate when the user is awake. These
variables depend on the physiology of a person and thus a machine
learning algorithm is trained with user-specific input to determine when
he/she is awake/asleep and determine from that the exact parameters that
cause the algorithm to deem someone asleep.

[0134] In an exemplary configuration, the light detectors may be
positioned on the underside of the wearable system and all of the heart
rate sensors may be positioned adjacent to each other. For example, the
low power sensor(s) may be adjacent to the high power sensor(s) as the
sensors may be chosen and placed where the strongest signal occurs. In
one example configuration, a 3-axis accelerometer may be used that is
located on the top part of the wearable system.

[0135] In some embodiments, the processing module may be configured to
automatically adjust a rate at which data is transmitted by the wireless
transmitter to minimize power consumption while ensuring that raw and
processed data generated by the system is reliably transmitted to
external computing devices. In one embodiment, the processing module
determines an amount of data to be transmitted (e.g., based on the amount
of data generated since the time of the last data transmission), and may
select the next data transmission time based on the amount of data to be
transmitted. For example, if it is determined that the amount of data
exceeds (or is equal to or greater than) a threshold level, the
processing module may transmit the data or may schedule a time for
transmitting the data. On the other hand, if it is determined that the
amount of data does not exceed (or is equal to or lower than) the
threshold level, the processing module may postpone data transmission to
minimize power consumption by the transmitter. In one non-limiting
example, the threshold may be set to the amount of data that may be sent
in two seconds under current conditions. Exemplary data transmission
rates may range from about 50 kbytes per second to about 1 Mbyte per
second, but are not limiting to this exemplary range.

[0136] In some embodiments, an operational characteristic of the
microprocessor may be automatically adjusted to minimize power
consumption. This adjustment may be based on a level of motion of the
user's body.

III. EXEMPLARY PHYSIOLOGICAL ANALYTICS SYSTEM

[0137] Exemplary embodiments provide an analytics system for enabling
qualitative and quantitative monitoring and interpretation regarding a
user's body, health and physical training. The analytics system is
implemented in computer-executable instructions encoded on one or more
non-transitory computer-readable media. The analytics system relies on
and uses continuous or discontinuous data on one or more physiological
parameters including, but not limited to, heart rate. The data used by
the analytics system may be obtained or derived from an exemplary
physiological measurement system disclosed herein, or may be obtained or
derived from a derived source or system, for example, a database of
physiological data. In some embodiments, the analytics system computes,
stores and displays one or more indicators or scores relating to the
user's body, health and physical training including, but not limited to,
an intensity score and a recovery score. The scores may be updated in
real-time and continuously or at specific time periods, for example, the
recovery score may be determined every morning upon waking up, the
intensity score may be determined in real-time or after a workout routine
or for an entire day.

[0138] In certain exemplary embodiments, a fitness score may be
automatically determined based on the physiological data of two or more
users of exemplary wearable systems.

[0139] An intensity score or indicator provides an accurate indication of
the cardiovascular intensities experienced by the user during a portion
of a day, during the entire day or during any desired period of time
(e.g., during a week or month). The intensity score is customized and
adapted for the unique physiological properties of the user and takes
into account, for example, the user's age, gender, anaerobic threshold,
resting heart rate, maximum heart rate, and the like. If determined for
an exercise routine, the intensity score provides an indication of the
cardiovascular intensities experienced by the user continuously
throughout the routine. If determined for a period of including and
beyond an exercise routine, the intensity score provides an indication of
the cardiovascular intensities experienced by the user during the routine
and also the activities the user performed after the routine (e.g.,
resting on the couch, active day of shopping) that may affect their
recovery or exercise readiness.

[0140] In exemplary embodiments, the intensity score is calculated based
on the user's heart rate reserve (HRR) as detected continuously
throughout the desired time period, for example, throughout the entire
day. In one embodiment, the intensity score is an integral sum of the
weighted HRR detected continuously throughout the desired time period.
FIG. 10 is a flowchart illustrating an exemplary method of determining an
intensity score.

[0141] In step 1002, continuous heart rate readings are transformed or
converted to HRR values. A time series of heart rate data used in step
1002 may be denoted as:

[0142] HεT

[0143] A time series of HRR
measurements, v(t), may be defined in the following expression in which
MHR is the maximum heart rate and RHR is the resting heart rate of the
user:

[0143] v ( t ) = H ( t ) - RHR MHR - RHR ##EQU00002##

[0144] In step 1004, the HRR values are weighted according to a suitable
weighting scheme. Cardiovascular intensity, indicated by an intensity
score, is defined in the following expression in which w is a weighting
function of the HRR measurements:

I(t0,t1)=∫t0t1w(v(t))dt

[0145] In step 1006, the weighted time series of HRR values is summed and
normalized.

[0147] In step 1008, the summed and normalized values are scaled to
generate user-friendly intensity score values. That is, the unit interval
is transformed to have any desired distribution in a scale (e.g., a scale
including 21 points from 0 to 21), for example, arctangent, sigmoid,
sinusoidal, and the like. In certain distributions, the intensity values
increase at a linear rate along the scale, and in others, at the highest
ranges the intensity values increase at more than a linear rate to
indicate that it is more difficult to climb in the scale toward the
extreme end of the scale. In some embodiments, the raw intensity scores
are scaled by fitting a curve to a selected group of "canonical" exercise
routines that are predefined to have particular intensity scores.

[0148] In one embodiment, monotonic transformations of the unit interval
are achieved to transform the raw HRR values to user-friendly intensity
scores. An exemplary scaling scheme, expressed as f: [0, 1]→[0,
1], is performed using the following function:

[0149] To generate an intensity score, the resulting value may be
multiplied by a number based on the desired scale of the intensity score.
For example, if the intensity score is graduated from zero to 21, then
the value may be multiplied by 21.

[0150] In step 1010, the intensity score values are stored on a
non-transitory storage medium for retrieval, display and usage. In step
1012, the intensity score values are, in some embodiments, displayed on a
user interface rendered on a visual display device. The intensity score
values may be displayed as numbers and/or with the aid of graphical
tools, e.g., a graphical display of the scale of intensity scores with
current score, and the like. In some embodiments, the intensity score may
be indicated by audio. In step 1012, the intensity score values are, in
some embodiments, displayed along with one or more quantitative or
qualitative pieces of information on the user including, but not limited
to, whether the user has exceeded his/her anaerobic threshold, the heart
rate zones experienced by the user during an exercise routine, how
difficult an exercise routine was in the context of the user's training,
the user's perceived exertion during an exercise routine, whether the
exercise regimen of the user should be automatically adjusted (e.g., made
easier if the intensity scores are consistently high), whether the user
is likely to experience soreness the next day and the level of expected
soreness, characteristics of the exercise routine (e.g., how difficult it
was for the user, whether the exercise was in bursts or activity, whether
the exercise was tapering, etc.), and the like. In one embodiment, the
analytics system may automatically generate, store and display an
exercise regimen customized based on the intensity scores of the user.

[0151] Step 1004 may use any of a number of exemplary static or dynamic
weighting schemes that enable the intensity score to be customized and
adapted for the unique physiological properties of the user. In one
exemplary static weighting scheme, the weights applied to the HRR values
are based on static models of a physiological process. The human body
employs different sources of energy with varying efficiencies and
advantages at different HRR levels. For example, at the anaerobic
threshold (AT), the body shifts to anaerobic respiration in which the
cells produce two adenosine triphosphate (ATP) molecules per glucose
molecule, as opposed to 36 at lower HRR levels. At even higher HRR
levels, there is a further subsequent threshold (CPT) at which creatine
triphosphate (CTP) is employed for respiration with even less efficiency.

[0152] In order to account for the differing levels of cardiovascular
exertion and efficiency at the different HRR levels, in one embodiment,
the possible values of HRR are divided into a plurality of categories,
sections or levels (e.g., three) dependent on the efficiency of cellular
respiration at the respective categories. The HRR parameter range may be
divided in any suitable manner, such as, piecewise, including
piecewise-linear, piecewise-exponential, and the like. An exemplary
piecewise-linear division of the HRR parameter range enables weighting
each category with strictly increasing values. This scheme captures an
accurate indication of the cardiovascular intensity experienced by the
user because it is more difficult to spend time at higher HRR values,
which suggests that the weighting function should increase at the
increasing weight categories.

[0153] In one non-limiting example, the HRR parameter range may be
considered a range from zero (0) to one (1) and divided into categories
with strictly increasing weights. In one example, the HRR parameter range
may be divided into a first category of a zero HRR value and may assign
this category a weight of zero; a second category of HRR values falling
between zero (0) and the user's anaerobic threshold (AT) and may assign
this category a weight of one (1); a third category of HRR values falling
between the user's anaerobic threshold (AT) and a threshold (CPT) at
which the user's body employs creatine triphosphate for respiration and
may assign this category a weight of 18; and a fourth category of HRR
values falling between the creatine triphosphate threshold (CPT) and one
(1) and may assign this category a weight of 42, although other numbers
of HRR categories and different weight values are possible. That is, in
this example, the weights are defined as:

[0154] In another exemplary embodiment of the weighting scheme, the HRR
time series is weighted iteratively based on the intensity scores
determined thus far (e.g., the intensity score accrued thus far) and the
path taken by the HRR values to get to the present intensity score. The
path may be detected automatically based on the historical HRR values and
may indicate, for example, whether the user is performing high intensity
interval training (during which the intensity scores are rapidly rising
and falling), whether the user is taking long breaks between bursts of
exercise (during which the intensity scores are rising after longer
periods), and the like. The path may be used to dynamically determine and
adjust the weights applied to the HRR values. For example, in the case of
high intensity interval training, the weights applied may be higher than
in the case of a more traditional exercise routine.

[0155] In another exemplary embodiment of the weighting scheme, a
predictive approach is used by modeling the weights or coefficients to be
the coefficient estimates of a logistic regression model. In this scheme,
a training data set is obtained by continuously detecting the heart rate
time series and other personal parameters of a group of individuals. The
training data set is used to train a machine learning system to predict
the cardiovascular intensities experienced by the individuals based on
the heart rate and other personal data. The trained system models a
regression in which the coefficient estimates correspond to the weights
or coefficients of the weighting scheme. In the training phase, user
input on perceived exertion and the intensity scores are compared. The
learning algorithm also alters the weighs based on the improving or
declining health of a user as well as their qualitative feedback. This
yields a unique algorithm that incorporates physiology, qualitative
feedback, and quantitative data. In determining a weighting scheme for a
specific user, the trained machine learning system is run by executing
computer-executable instructions encoded on one or more non-transitory
computer-readable media, and generates the coefficient estimates which
are then used to weight the user's HRR time series.

[0156] One of ordinary skill in the art will recognize that two or more
aspects of any of the disclosed weighting schemes may be applied
separately or in combination in an exemplary method for determining an
intensity score.

[0157] A recovery score or indicator provides an accurate indication of
the level of recovery of a user's body and health after a period of
physical exertion. The human autonomic nervous system controls the
involuntary aspects of the body's physiology and is typically subdivided
into two branches: parasympathetic (deactivating) and sympathetic
(activating). Heart rate variability (HRV), i.e., the fluctuation in
inter-heartbeat interval time, is a commonly studied result of the
interplay between these two competing branches. Parasympathetic
activation reflects inputs from internal organs, causing a decrease in
heart rate. Sympathetic activation increases in response to stress,
exercise and disease, causing an increase in heart rate. For example,
when high intensity exercise takes place, the sympathetic response to the
exercise persists long after the completion of the exercise. When high
intensity exercise is followed by insufficient recovery, this imbalance
lasts typically until the next morning, resulting in a low morning HRV.
This result should be taken as a warning sign as it indicates that the
parasympathetic system was suppressed throughout the night. While
suppressed, normal repair and maintenance processes that ordinarily would
occur during sleep were suppressed as well. Suppression of the normal
repair and maintenance processes results in an unprepared state for the
next day, making subsequent exercise attempts more challenging.

[0158] The recovery score is customized and adapted for the unique
physiological properties of the user and takes into account, for example,
the user's heart rate variability (HRV), resting heart rate, sleep
quality and recent physiological strain (indicated, in one example, by
the intensity score of the user). In one exemplary embodiment, the
recovery score is a weighted combination of the user's heart rate
variability (HRV), resting heart rate, sleep quality indicated by a sleep
score, and recent strain (indicated, in one example, by the intensity
score of the user). In an exemplar, the sleep score combined with
performance readiness measures (such as, morning heart rate and morning
heart rate variability) provides a complete overview of recovery to the
user. By considering sleep and HRV alone or in combination, the user can
understand how exercise-ready he/she is each day and to understand how
he/she arrived at the exercise-readiness score each day, for example,
whether a low exercise-readiness score is a predictor of poor recovery
habits or an inappropriate training schedule. This insight aids the user
in adjusting his/her daily activities, exercise regimen and sleeping
schedule therefore obtain the most out of his/her training.

[0159] In some cases, the recovery score may take into account perceived
psychological strain experienced by the user. In some cases, perceived
psychological strain may be detected from user input via, for example, a
questionnaire on a mobile device or web application. In other cases,
psychological strain may be determined automatically by detecting changes
in sympathetic activation based on one or more parameters including, but
not limited to, heart rate variability, heart rate, galvanic skin
response, and the like.

[0160] With regard to the user's HRV used in determining the recovery
score, suitable techniques for analyzing HRV include, but are not limited
to, time-domain methods, frequency-domain methods, geometric methods and
non-linear methods. In one embodiment, the HRV metric of the
root-mean-square of successive differences of RR intervals (RMSSD) is
used. The analytics system may consider the magnitude of the differences
between 7-day moving averages and 3-day moving averages of these readings
for a given day. Other embodiments may use Poincare Plot analysis or
other suitable metrics of HRV.

[0161] With regard to the user's resting heart rate, moving averages of
the resting heart rate are analyzed to determine significant deviations.
Consideration of the moving averages is important since day-to-day
physiological variation is quite large even in healthy individuals.
Therefore, the analytics system may perform a smoothing operation to
distinguish changes from normal fluctuations.

[0162] Although an inactive condition, sleep is a highly active recovery
state during which a major portion of the physiological recovery process
takes place. Nonetheless, a small, yet significant, amount of recovery
can occur throughout the day by rehydration, macronutrient replacement,
lactic acid removal, glycogen re-synthesis, growth hormone production and
a limited amount of musculoskeletal repair. In assessing the user's sleep
quality, the analytics system generates a sleep score using continuous
data collected by an exemplary physiological measurement system regarding
the user's heart rate, skin conductivity, ambient temperature and
accelerometer/gyroscope data throughout the user's sleep. Collection and
use of these four streams of data enable an understanding of sleep
previously only accessible through invasive and disruptive over-night
laboratory testing. For example, an increase in skin conductivity when
ambient temperature is not increasing, the wearer's heart rate is low,
and the accelerometer/gyroscope shows little motion, may indicate that
the wearer has fallen asleep. The sleep score indicates and is a measure
of sleep efficiency (how good the user's sleep was) and sleep duration
(if the user had sufficient sleep). Each of these measures is determined
by a combination of physiological parameters, personal habits and daily
stress/strain (intensity) inputs. The actual data measuring the time
spent in various stages of sleep may be combined with the wearer's recent
daily history and a longer-term data set describing the wearer's personal
habits to assess the level of sleep sufficiency achieved by the user. The
sleep score is designed to model sleep quality in the context of sleep
duration and history. It thus takes advantage of the continuous
monitoring nature of the exemplary physiological measurement systems
disclosed herein by considering each sleep period in the context of
biologically-determined sleep needs, pattern-determined sleep needs and
historically-determined sleep debt.

[0163] The recovery and sleep score values are stored on a non-transitory
storage medium for retrieval, display and usage. The recovery and/or
sleep score values are, in some embodiments, displayed on a user
interface rendered on a visual display device. The recovery and/or sleep
score values may be displayed as numbers and/or with the aid of graphical
tools, e.g., a graphical display of the scale of recovery scores with
current score, and the like. In some embodiments, the recovery and/or
sleep score may be indicated by audio. The recovery score values are, in
some embodiments, displayed along with one or more quantitative or
qualitative pieces of information on the user including, but not limited
to, whether the user has recovered sufficiently, what level of activity
the user is prepared to perform, whether the user is prepared to perform
an exercise routine a particular desired intensity, whether the user
should rest and the duration of recommended rest, whether the exercise
regimen of the user should be automatically adjusted (e.g., made easier
if the recovery score is low), and the like. In one embodiment, the
analytics system may automatically generate, store and display an
exercise regimen customized based on the recovery scores of the user
alone or in combination with the intensity scores.

[0164] FIG. 11 is a flowchart illustrating an exemplary method by which a
user may use intensity and recovery scores. In step 1102, the wearable
physiological measurement system begins determining heart rate
variability (HRV) measurements based on continuous heart rate data
collected by an exemplary physiological measurement system. In some
cases, it may take the collection of several days of heart rate data to
obtain an accurate baseline for the HRV. In step 1104, the analytics
system may generate and display intensity score for an entire day or an
exercise routine. In some cases, the analytics system may display
quantitative and/or qualitative information corresponding to the
intensity score. FIG. 12 illustrates an exemplary display of an intensity
score index indicated in a circular graphic component with an exemplary
current score of 19.0 indicated. The graphic component may indicate a
degree of difficulty of the exercise corresponding to the current score
selected from, for example, maximum all out, near maximal, very hard,
hard, moderate, light, active, light active, no activity, asleep, and the
like. The display may indicate, for example, that the intensity score
corresponds to a good and tapering exercise routine, that the user did
not overcome his anaerobic threshold and that the user will have little
to no soreness the next day.

[0165] In step 1106, in an exemplary embodiment, the analytics system may
automatically generate or adjust an exercise routine or regimen based on
the user's actual intensity scores or desired intensity scores. For
example, based on inputs of the user's actual intensity scores, a desired
intensity score (that is higher than the actual intensity scores) and a
first exercise routine currently performed by the user (e.g., walking),
the analytics system may recommend a second different exercise routine
that is typically associated with higher intensity scores than the first
exercise routine (e.g., running). The exercise routine may be displayed
on a display device.

[0166] In step 1108, at any given time during the day (e.g., every
morning), the analytics system may generate and display a recovery score.
In some cases, the analytics system may display quantitative and/or
qualitative information corresponding to the intensity score. For
example, in step 1110, in an exemplary embodiment, the analytics system
may determine if the recovery is greater than (or equal to or greater
than) a first predetermined threshold (e.g., about 60% to about 80% in
some examples) that indicates that the user is recovered and is ready for
exercise. If this is the case, in step 1112, the analytics system may
indicate that the user is ready to perform an exercise routine at a
desired intensity or that the user is ready to perform an exercise
routine more challenging than the past day's routine. Otherwise, in step
1114, the analytics system may determine if the recovery is lower than
(or equal to or lower than) a second predetermined threshold (e.g., about
10% to about 40% in some examples) that indicates that the user has not
recovered. If this is the case, in step 1116, the analytics system may
indicate that the user should not exercise and should rest for an
extended period. The analytics system may, in some cases, the duration of
recommended rest. Otherwise, in step 1118, the analytics system may
indicate that the user may exercise according to his/her exercise regimen
while being careful not to overexert him/herself. The thresholds may, in
some cases, be adjusted based on a desired intensity at which the user
desires to exercise. For example, the thresholds may be increased for
higher planned intensity scores.

[0167] FIG. 13 illustrates an exemplary display of a recovery score index
indicated in a circular graphic component with a first threshold of 66%
and a second threshold of 33% indicated. FIGS. 14A-14C illustrate the
recovery score graphic component with exemplary recovery scores and
qualitative information corresponding to the recovery scores.

[0168] Optionally, in an exemplary embodiment, the analytics system may
automatically generate or adjust an exercise routine or regimen based on
the user's actual recovery scores (e.g., to recommend lighter exercise
for days during which the user has not recovered sufficiently). This
process may also use a combination of the intensity and recovery scores.

[0169] The analytics system may, in some embodiments, determine and
display the intensity and/or recovery scores of a plurality of users in a
comparative manner. This enables users to match exercise routines with
others based on comparisons among their intensity scores.

IV. EXEMPLARY DISPLAYS AND USER INTERFACES

[0170] An aspect of the present invention is directed to providing an
online website for health and fitness monitoring. Exemplary embodiments
also provide a vibrant and interactive online community for displaying
and sharing physiological data among users. The website allows users to
monitor their own fitness results, share information with their teammates
and coaches, compete with other users, and win status. The website may be
configured to provide an interactive user interface. The website may be
configured to display results based on analysis on physiological data
associated with one or more users. The website may be configured to
provide competitive ways to compare one user to another, and ultimately a
more interactive experience for the user. For example, in some
embodiments, instead of merely comparing a user's physiological data and
performance relative to that user's past performances, the user may be
allowed to compete with other users and the user's performance may be
compared to that of other users.

[0171] A user of the website may include an individual whose health or
fitness is being monitored, such as an individual wearing a bracelet
disclosed herein, an athlete, a sports team member, a personal trainer or
a coach. In some embodiments, a user may pick their own trainer from a
list to comment on their performance.

[0172] In certain embodiments, the physiological data may be obtained,
directly or indirectly, from a wearable physiological measurement system
as disclosed herein. In other embodiments, the physiological data may be
obtained from any other suitable system (e.g., an ECG system) or storage
device (e.g., a physiological database). Exemplary wearable physiological
measurement systems have the ability to stream physiological information
wirelessly, directly or through a mobile device application and/or
through a cloud-based storage system, to an online website. Both the
wearable system and the website allow a user to provide feedback
regarding his day, which enables recovery and performance ratings.

[0173] In some embodiments, the website may be a mobile website or a
mobile application. In some embodiments, the website may be configured to
communicate data to other websites, devices or applications.

[0174] The exemplary website may require a brief and free sign-up process
during which a user may create an account with his/her name, account
name, email, home address, height, weight, age, and a unique code
provided in his/her wearable physiological measurement system. The unique
code may be provided, for example, on the wearable system itself or in
the packaged kit. Once subscribed, continuous physiological data received
from the user's system may be retrieved in a real-time continuous basis
and presented automatically on a webpage associated with the user.
Alternatively, updated data may be displayed upon a user prompt or
periodically. Additionally, the user can add information to his profile,
such as, a picture, favorite activities, sports team(s), and the user may
search for teammates/friends on the website for sharing information.

[0175] FIGS. 15A-18B illustrate an exemplary user interface 1500 for
displaying physiological data specific to a user as rendered on visual
display device. The user interface 1500 may take the form of a webpage in
some embodiments. One of ordinary skill in the art will recognize that
the information in FIGS. 15-18 represent non-limiting illustrative
examples. One of ordinary skill in the art will recognize that the
particular types of information disclosed with respect to FIGS. 15A-18B
are exemplary and non-limiting. The user interface 1500 may include a
summary panel 1502 including an identification 1504 of the user (e.g., a
real or account name) with, optionally, a picture or photo corresponding
to the user. The summary panel 1502 may also display the current
intensity score 1506 and the current recovery score 1508 of the user. In
some embodiments, the summary panel 1502 may display the number of
calories burned by the user 1510 that day and the number of hours of
sleep 1512 obtained by the user the previous night.

[0176] The user interface 1500 may also include panels for presenting
information on the user's workouts--a workout panel 1514 accessible using
tab 1516, day--a day panel 1518 accessible using tab 1520, and sleep--a
sleep panel 1522 accessible using tab 1524. The same or different
feedback panels may be associated with the workout, day and sleep panels.
The panels may enable the user to select and customize one or more
informative panels that appear in his/her user interface display.

[0177] The workout panel 1514 may present quantitative information on the
user's health and exercise routines, for example, a graph 1530 of the
user's continuous heart rate during the exercise, statistics 1532 on the
maximum heart rate, average heart rate, duration of exercise, number of
steps taken and calories expended, zones 1534 in which the maximum heart
rate fell during the exercise, and a graph 1536 of the intensity scores
over a period of time (e.g., seven days).

[0178] A feedback panel 1538 associated with the workout panel 1514 may
present information on the intensity score and the exercise routines
performed by the user during a selected period of time including, but not
limited to, quantitative information, qualitative information, feedback,
recommendations on future exercise routines, and the like. The feedback
panel 1538 may present the intensity score along with a qualitative
summary 1540 of the score indicating, for example, whether the user
pushed past his anaerobic threshold for a considerable period of the
exercise, whether the exercise is likely to cause muscle pain and
soreness, and the like. Based on analysis of the quantitative health
parameters monitored during the exercise routine, the feedback panel 1538
may present one or more tips 1542 on adjusting the exercise routine, for
example, that the exercise routine started too rapidly and that the user
should warm up for longer. In some cases, upon selection of the tips
sub-panel 1542, a corresponding indicator 1544 may be provided in the
heart rate graph 1530.

[0179] Based on analysis of the quantitative health parameters monitored
during the exercise routine, the feedback panel 1538 may also present
qualitative information 1545 on the user's exercise routine, for example,
comparison of the present day's exercise routine to the user's historical
exercise data. Such information may indicate, for example, that the
user's maximum heart rate for the day's exercise was the highest ever
recorded, that the steps taken by the user that day was the fewest ever
recorded, that the user burned a lot of calories and that more calories
may be burned by lowering the intensity of the exercise, and the like.
The feedback panel 1538 may also present cautionary indicators 1546 to
warn the user of future anticipated health events, for example, the
likelihood of soreness (e.g., if the intensity score is higher than a
predefined threshold), and the like.

[0180] An exemplary analytics system may analyze the information presented
in the workout panel 1514 and automatically determine whether the user
performed a specific exercise routine or activity. As one example, given
a small number of steps taken and a high calorie burn and heart rate, the
system may determine that it is possible the user rode a bicycle that
day. In some cases, the feedback panel 1538 may prompt the user to
confirm whether he/she indeed performed that activity in a user input
field 1548. This user input may be displayed and/or used to improve an
understanding of the user's health and exercise routines.

[0181] The day panel 1518 may include information on health parameters of
the user during the current day including, but not limited to, the number
of calories burned and the number of calories taken in 1550 (which may be
based on user input on the foods eaten), a graph 1554 of the day's
continuous heart rate, statistics 1556 on the resting heart rate and
steps taken by the user that day, a graph 1558 of the calories burned
that and other days, and the like.

[0182] In some cases, an analytics system may analyze the physiological
data (e.g., heart rate data) and estimate the durations of sleep,
activity and workout during the day. A feedback panel 1562 associated
with the day panel 1518 may present these durations 1564. In some cases,
the feedback panel 1562 may display a net number of calories consumed by
the user that day 1566. Based on analysis of the quantitative health
parameters monitored during the exercise routine, the feedback panel 1562
may also present qualitative information 1568 on the user's exercise
routine. Such information may indicate, for example, that the user was
stressed at a certain point in the day (e.g., if there was a high level
of sweat with little activity), that the user's maximum heart rate for
the day's exercise was the highest ever recorded, that the steps taken by
the user that day was the fewest ever recorded, that the user burned a
lot of calories and that more calories may be burned by lowering the
intensity of the exercise, and the like. The feedback panel 1562 may also
present cautionary indicators 1570 to warn the user of future anticipated
health events, for example, tachycardia, susceptibility to illness or
overtraining (e.g., if the resting heart rate is elevated for a few
days), and the like.

[0183] An exemplary analytics system may analyze the information presented
in the day panel 1518 and automatically determine whether the user
performed a specific exercise routine or activity. As one example, given
an elevated heart rate with little activity, the system may determine
that it is possible the user drank coffee at that point. In some cases,
the feedback panel 1562 may prompt the user to confirm whether he/she
indeed performed that activity in a user input field 1572. This user
input may be displayed and/or used to improve an understanding of the
user's health and exercise routines.

[0184] The sleep panel 1522 may include information on health parameters
of the user during sleep including, but not limited to, an overlaid graph
1573 of heart rate and movement during sleep, statistics 1574 on the
maximum heart rate, minimum heart rate, number of times the user awoke
during sleep, average movement during sleep, a sleep cycle indicator 1576
showing durations spent awake, in light sleep, in deep sleep and in REM
sleep, and a sleep duration graph 1578 showing the number of hours slept
over a period of time.

[0185] A feedback panel 1580 associated with the sleep panel 1522 may
present information on the user's sleep including, but not limited to,
quantitative information, qualitative information, feedback,
recommendations on future exercise routines, and the like. The feedback
panel 1580 may present a sleep score and/or a number of hours of sleep
along with a qualitative summary of the score 1582 indicating, for
example, whether the user slept enough, whether the sleep was efficient
or inefficient, whether the user moved around and how much during sleep,
and the like. Based on analysis of the quantitative health parameters
monitored during sleep, the feedback panel 1580 may present one or more
tips 1584 on adjusting sleep, for example, that the woke up a number of
times during sleep and that user can try to sleep on his side rather than
on his back.

[0186] Based on analysis of the quantitative health parameters monitored
during the exercise routine, the feedback panel 1580 may also present
qualitative information 1586 on the user's sleep. Such information may
indicate, for example, that the user's maximum heart rate for the day's
exercise was the highest ever recorded during sleep. The feedback panel
1580 may also present cautionary indicators 1588 to warn the user of
future anticipated health events, for example, a sign of overtraining and
a recommendation to get more sleep (e.g., if the user awoke many times
during sleep and/or if the user moved around during sleep.

[0187] The user interface 1500 may provide a user input field 1590 for
enabling the user to indicate his/her feelings on, for example, the
activities performed, perceived exertion, energy level, performance. The
user interface 1500 may also provide a user input field 1592 for enabling
the user to indicate other facts about his exercise routine, e.g.,
comments on what the user was doing at a specific point in the exercise
routine with a link 1594 to a corresponding point in the heart rate graph
1530. In some embodiments, the user may specify a route and/or location
on a map at which the exercise routine was performed.

[0188] Exemplary embodiments also enable a user to compare his/her
quantitative and/or qualitative physiological data with those of one or
more additional users. A user may be presented with user selection
components representing other users whose data is available for display,
as shown in exemplary user interface 2100 in FIG. 21. When a pointer is
hovered over a user selection component (e.g., an icon representing a
user), a snapshot of the user's information is presented in a popup
component, and clicking on the user selection component opens up the full
user interface displaying the user's information. In some cases, the user
selection components include certain user-specific data surrounding an
image representing the user, for example, a graphic element indicating
the user's intensity score. The user selection components may be provided
in a grid as shown or in a linear listing for easier sorting. The users
appearing in the user selection components may be sorted and/or ranked
based on any desired criteria, e.g., intensity scores, who is
experiencing soreness, and the like. A user may leave comments on other
users' pages. Similarly, a user may select privacy settings to indicate
which aspects of his/her own data may be viewed by other users.

[0189] FIGS. 19A and 19B illustrate an exemplary user interface 1900
rendered on a visual display device for displaying physiological data on
a plurality of users. In some cases, a user may freely compare the data
of any users whose data is available and accessible, i.e., set to an
appropriate privacy level. In some cases, comparative data may correspond
to a plurality of users who may be grouped together based on any suitable
criteria, e.g., members of a gym, military team, and the like. In some
cases, the user may be able to discover other users or comparable data by
searching or performing queries on any desired parameters, for example,
workouts, activities, age groups, locations, intensities, recoveries and
the like. For example, a user may perform a query for "Workouts above a
17 Intensity in Boston for runners my age." The exemplary user interface
may also identify or suggest users with whom to exchange data based on
similar parameters. Data on any number of users may be presented and
compared including, but not limited to, 2, 3, 4, 5, 6, 7, 8, 9, 10, and
the like.

[0190] In a default option, data from the same time period(s) may be
presented for all of the users. In some embodiments, time periods for
each user may be selected independently and data from the selected time
periods may be displayed in a comparative manner on the same user
interface, e.g., in one or more overlaid graphs. FIG. 20 illustrates a
user interface 2000 that may be used to independently select time periods
of data for each of five users so that the data from the selected periods
may be displayed together. The user interface 2000 includes a
representation of each user 2002a-2002e, optionally an indication of each
user's intensity score, a calendar component 2004 for selecting the time
periods, and a component 2006a-2006e indicating the time periods selected
for each user. In some cases, data from different time periods (but, for
example, for the same time duration) for the same user may be presented
on the same user interface for comparative purpose, for example, to
determine training progress.

[0191] In FIGS. 19A and 19B, the user interface 1900 may include a summary
panel 1902 including an identification 1904a-1904b of the users (e.g., a
real or account name) with, optionally, a picture or photo corresponding
to the user. In some cases, the summary panel 1902 may also display
certain information associated with the users, for example, their
intensity scores.

[0192] A workout panel 1908 may present quantitative information on the
users' health and exercise routines, for example, an overlaid graph 1910
of the users' continuous heart rate during the exercise, statistics 1912
on the users' maximum heart rate, average heart rate, duration of
exercise, number of steps taken and calories expended, zones 1914 in
which the users' maximum heart rate fell during the exercise, and an
overlaid graph 1916 of the users' intensity scores over a period of time
(e.g., seven days).

[0193] A feedback panel 1918 associated with the workout panel 1908 may
present comparative qualitative information on the users' exercise
routines including, but not limited to, whether the users were working
out at the same time, which user had a more difficult workout, the
comparative efficiencies of the users, and the like. Similarly, a day
panel and a sleep panel may present comparative information for the
selected users.

[0194] The analytics system may analyze comparative data among a plurality
of users and provide rankings of individuals, teams and groups of
individuals (e.g., employees of a company, members of a gym) based on,
for example, average intensity scores. For each user, the analytics
system may calculate and display percentile rankings of the user with
respect to all of the users in a community in terms of, for example,
intensity scores, quality of sleep, and the like.

[0195] Exemplary embodiments also provide user interfaces to enable
intuitive and efficient monitoring of a plurality of users by an
individual with administrative powers to view the users' health data.
Such an administrative user may be a physical instructor, trainer or
coach who may use the interface to manage his/her clients' workout
regimen.

[0196] FIGS. 21A and 21B illustrate an exemplary user interface 2100
viewable by an administrative user, including a selectable and editable
representation or listing 2102 of the users (e.g., a trainer's clients)
whose health information is available for display. When a pointer is
hovered over a user selection component (e.g., an icon representing a
user), a snapshot of the user's information is presented in a popup
component, and clicking on the user selection component opens up the full
user interface displaying the user's information. In some cases, the user
selection components include certain user-specific data surrounding an
image representing the user, for example, a graphic element indicating
the user's intensity score. The user selection components in the listing
2102 may be provided in a grid as shown or in a linear listing for easier
sorting. The users appearing in the listing 2102 may be sorted and/or
ranked based on any desired criteria, e.g., intensity scores, who is
experiencing soreness, and the like. Selection of any one user causes the
user interface specific to that user to be opened, for example, as shown
in FIGS. 15A-18B. The administrative user may leave messages on the user
interfaces of the different users. Selection of more than one user causes
a user interface comparing the selected users to be opened, for example,
as shown in FIGS. 19A and 19B.

[0197] The administrative user interface 2100 may include a listing of
users 2104 who recently performed exercise routines including the time of
their last workout and their intensity scores, a listing of users 2106
who are off-schedule in their exercise regimen and how many days they
have not been exercising, a listing of users 2108 who are experiencing
soreness (that may be determined automatically based on intensity
scores), a listing of users who are sleep-deprived (that may be
determined automatically based on sleep data), and the like. The lists
may be ordered in some cases. The user interface 2100 may also display a
calendar or portion of a calendar 2110 indicating training times for
different users. The calendar feature enables the administrative user to
review exercise schedules over time and understand how well individuals
or teams are meeting goals. For example, the administrative user may
determine that an individual is undertraining if his intensity for the
day was 18 whereas the team average was 14.

[0198] In any of the exemplary user interfaces disclosed herein, color
coding may be used to indicate categories of any parameter. For example,
in a day panel of a user interface, color coding may be used to indicate
whether a user's day was difficult (e.g., with the color red), tapering
(e.g., with the color yellow), or a day off from training (e.g., with the
color blue).

[0199] Exemplary embodiments enable selected qualitative and/or
quantitative data from any of the user interfaces disclosed herein to be
selected, packaged and exported to an external application, computational
device or webpage (e.g., a blog) for display, storage and analysis. The
data may be selected based on any desired characteristic including, but
not limited to, gender, age, location, activity, intensity level, and any
combinations thereof. An online blog may be presented to display the data
and allow users to comment on the data.

V. EXEMPLARY COMPUTING DEVICES

[0200] Various aspects and functions described herein in accord with the
present invention may be implemented as hardware, software or a
combination of hardware and software on one or more computer systems.
Exemplary computer systems that may be used include, but are not limited
to, personal computers, embedded computing systems, network appliances,
workstations, mainframes, networked clients, servers, media servers,
application servers, database servers, web servers, virtual servers, and
the like. Other examples of computer systems that may be used include,
but are not limited to, mobile computing devices, such as wearable
devices, cellular phones and personal digital assistants, and network
equipment, such as load balancers, routers and switches.

[0201] FIG. 22 is a block diagram of an exemplary computing device 2200
that may be used to perform any of the methods provided by exemplary
embodiments. The computing device may be configured as an embedded system
in the integrated circuit board(s) of a wearable physiological
measurements system and/or as an external computing device that may
receive data from a wearable physiological measurement system.

[0202] The computing device 2200 includes one or more non-transitory
computer-readable media for storing one or more computer-executable
instructions or software for implementing exemplary embodiments. The
non-transitory computer-readable media may include, but are not limited
to, one or more types of hardware memory, non-transitory tangible media
(for example, one or more magnetic storage disks, one or more optical
disks, one or more USB flashdrives), and the like. For example, memory
2206 included in the computing device 2200 may store computer-readable
and computer-executable instructions or software for implementing
exemplary embodiments. The computing device 2200 also includes processor
2202 and associated core 2204, and optionally, one or more additional
processor(s) 2202' and associated core(s) 2204' (for example, in the case
of computer systems having multiple processors/cores), for executing
computer-readable and computer-executable instructions or software stored
in the memory 2206 and other programs for controlling system hardware.
Processor 2202 and processor(s) 2202' may each be a single core processor
or multiple core (2204 and 2204') processor.

[0203] Virtualization may be employed in the computing device 2200 so that
infrastructure and resources in the computing device may be shared
dynamically. A virtual machine 2214 may be provided to handle a process
running on multiple processors so that the process appears to be using
only one computing resource rather than multiple computing resources.
Multiple virtual machines may also be used with one processor.

[0204] Memory 2206 may include a computer system memory or random access
memory, such as dynamic random-access memory (DRAM), static random-access
memory (SRAM), extended data output random-access memory (EDO RAM), and
the like. Memory 2206 may include other types of memory as well, or
combinations thereof.

[0205] A user may interact with the computing device 2200 through a visual
display device 2218, such as a computer monitor, which may display one or
more user interfaces 2220 that may be provided in accordance with
exemplary embodiments. The visual display device 2218 may also display
other aspects, elements and/or information or data associated with
exemplary embodiments, for example, views of databases, photos, and the
like. The computing device 2200 may include other input/output (I/O)
devices for receiving input from a user, for example, a keyboard or any
suitable multi-point touch interface 2208, a pointing device 2210 (e.g.,
a mouse). The keyboard 2208 and the pointing device 2210 may be coupled
to the visual display device 2218. The computing device 2200 may include
other suitable conventional I/O peripherals.

[0206] The computing device 2200 may also include one or more storage
devices 2224, such as a hard-drive, CD-ROM, or other computer readable
media, for storing data and computer-readable instructions and/or
software that implement exemplary methods as taught herein. Exemplary
storage device 2224 may also store one or more databases 2226 for storing
any suitable information required to implement exemplary embodiments
(e.g., physiological data, computer-executable instructions for analyzing
the data, and the like). The databases may be updated by a user or
automatically at any suitable time to add, delete or update one or more
items in the databases.

[0207] The computing device 2200 may include a network interface 2212
configured to interface via one or more network devices 2222 with one or
more networks, for example, Local Area Network (LAN), Wide Area Network
(WAN) or the Internet through a variety of connections including, but not
limited to, standard telephone lines, LAN or WAN links (for example,
802.11, T1, T3, 56 kb, X.25), broadband connections (for example, ISDN,
Frame Relay, ATM), wireless connections, controller area network (CAN),
or some combination of any or all of the above. The network interface
2212 may include a built-in network adapter, network interface card,
personal computer memory card international associate (PCMCIA) network
card, card bus network adapter, wireless network adapter, universal
serial bus (USB) network adapter, modem or any other device suitable for
interfacing the computing device 2200 to any type of network capable of
communication and performing the operations described herein. Moreover,
the computing device 2200 may be any computer system, such as a
workstation, desktop computer, server, laptop, handheld computer, tablet
computer (e.g., the iPad® tablet computer), mobile computing or
communication device (e.g., the iPhone® communication device), or
other form of computing or telecommunications device that is capable of
communication and that has sufficient processor power and memory capacity
to perform the operations described herein.

[0208] The wearable physiological measurement system may record and
transmit at least the following types of data to an external computing
system, mobile communication system, a cloud or non-cloud storage system,
and/or the Internet: raw physiological data (e.g., heart rate data,
movement data, galvanic skin response data) and processed data derived
from the raw data (e.g., RR intervals determined from the heart rate
data). Transmission modes may be wired (e.g., using USB stick inserted
into a USB port on the system) or wireless (e.g., using a wireless
transmitter). The raw and processed data may be transmitted together or
separately using the same or different transmission modes. Since a raw
data file is typically substantially larger than a processed data file,
in one non-limiting example, the raw data file may be transmitted using
WiFi or a USB stick, while the processed data file may be transmitted
using Bluetooth.

[0209] An exemplary wearable system may include a 2G, 3G or 4G chip that
wirelessly uploads all data to the website disclosed herein without
requiring any other external device. A 3G or 4G chip may be used
preferably as a 2G connection on a Nokia 5800 was found to transfer data
at a rate of 520 kbps using 1.69 W of power, while a 3G connection
transferred at 960 kbps using 1.73 W of power. That is, the 3G chip was
found to use negligibly more power for almost twice the transfer speed,
thereby halving half the transfer time and using much less energy from
the battery.

[0210] In some cases, the wearable system may opportunistically transfer
data when in close proximity to a streaming outlet. For example, the
system may avoid data transmission when it is not within close proximity
of a streaming outlet, and, when nearby a streaming outlet (e.g., a
linked phone), may send the data to the external device via Bluetooth and
to the Internet via the external device. This is both convenient and
"free" in the sense that the system utilizes existing cellular data
plans.

[0211] Limiting the frequency at which data is streamed increases the
wearable system's battery life. In one non-limiting example, the system
may be set to stream automatically at a certain time of the day (e.g., in
the morning) and following a time-stamp. Regardless of the data
transmission scheme, the system stores all the data it collects. Data may
also be streamed on demand by a user, for example, by turning a physical
component on the system and holding it or by initiating a process on a
mobile application or receiving device. In some embodiments, the data
transmission frequency may be automatically adjusted based on one or more
physiological parameters, e.g., heart rate. For example, higher heart
rates may prompt more frequent and real-time streaming transmission of
data.

[0212] The computing device 2200 may run any operating system 2216, such
as any of the versions of the Microsoft® Windows® operating
systems, the different releases of the Unix and Linux operating systems,
any version of the MacOS® for Macintosh computers, any embedded
operating system, any real-time operating system, any open source
operating system, any proprietary operating system, any operating systems
for mobile computing devices, or any other operating system capable of
running on the computing device and performing the operations described
herein. In exemplary embodiments, the operating system 2216 may be run in
native mode or emulated mode. In an exemplary embodiment, the operating
system 2216 may be run on one or more cloud machine instances.

VI. EXEMPLARY NETWORK ENVIRONMENTS

[0213] Various aspects and functions of the present invention may be
distributed among one or more computer systems configured to provide a
service to one or more client computers, or to perform an overall task as
part of a distributed system. Additionally, aspects may be performed on a
client-server or multi-tier system that includes components distributed
among one or more server systems that perform various functions. Thus,
the present invention is not limited to executing on any particular
system or group of systems. Further, aspects may be implemented in
software, hardware or firmware, or any combination thereof. Thus, aspects
in accord with the present invention may be implemented within methods,
acts, systems, system placements and components using a variety of
hardware and software configurations, and the invention is not limited to
any particular distributed architecture, network or communication
protocol. Furthermore, aspects in accord with the present invention may
be implemented as specially-programmed hardware and/or software.

[0214] FIG. 23 is a block diagram of an exemplary distributed computer
system 2300 in which various aspects and functions in accord with the
present invention may be practiced. The distributed computer system 2300
may include one or more computer systems. For example, as illustrated,
the distributed computer system 2300 includes three computer systems
2302, 2304 and 2306. As shown, the computer systems 2302, 2304, 2306 are
interconnected by, and may exchange data through, a communication network
2308. The network 2308 may include any communication network through
which computer systems may exchange data. To exchange data via the
network 2308, the computer systems and the network may use various
methods, protocols and standards including, but not limited to, token
ring, Ethernet, wireless Ethernet, Bluetooth, transmission control
protocol/internet protocol (TCP/IP), user datagram protocol (UDP),
hypertext transfer protocol (HTTP), file transfer protocol (FTP), simple
network management protocol (SNMP), short message service (SMS),
multimedia messaging service (MMS), signaling system no. 7 (SS7),
JavaScript Object Notation (JSON), extensible markup language (XML),
representational state transfer (REST), simple object access protocol
(SOAP), common object request broker architecture (CORBA), internet
inter-ORB protocol (IIOP), remote method invocation (RMI), distributed
component object model (DCOM), and Web Services. To ensure data transfer
is secure, the computer systems may transmit data via the network using a
variety of security measures including, but not limited to, transport
layer security (TSL), secure sockets layer (SSL) and virtual private
network (VPN). While the distributed computer system 2300 illustrates
three networked computer systems, the distributed computer system may
include any number of computer systems, networked using any medium and
communication protocol.

[0215] Various aspects and functions in accord with the present invention
may be implemented as specialized hardware or software executing in one
or more computer systems. As depicted, the computer system 2300 includes
a processor 2310, a memory 2312, a bus 2314, an interface 2316 and a
storage system 2318. The processor 2310, which may include one or more
microprocessors or other types of controllers, can perform a series of
instructions that manipulate data. The processor 2310 may be a well-known
commercially-available processor such as an Intel Pentium, Intel Atom,
ARM Processor, Motorola PowerPC, SGI MIPS, Sun UltraSPARC or
Hewlett-Packard PA-RISC processor, or may be any other type of processor
or controller as many other processors and controllers are available. The
processor 2310 may be a mobile device or smart phone processor, such as
an ARM Cortex processor, a Qualcomm Snapdragon processor or an Apple
processor. As shown, the processor 2310 is connected to other system
placements, including a memory 2312, by the bus 2314.

[0216] The memory 2312 may be used for storing programs and data during
operation of the computer system 2300. Thus, the memory 2312 may be a
relatively high performance, volatile, random access memory such as a
dynamic random access memory (DRAM) or static memory (SRAM). However, the
memory 2312 may include any device for storing data, such a disk drive or
other non-volatile storage device, such as flash memory or phase-change
memory (PCM). Various embodiments in accord with the present invention
can organize the memory 2312 into particularized and, in some cases,
unique structures to perform the aspects and functions disclosed herein.

[0217] Components of the computer system 2300 may be coupled by an
interconnection element such as the bus 2314. The bus 2314 may include
one or more physical busses (for example, buses between components that
are integrated within the same machine) and may include any communication
coupling between system placements including specialized or standard
computing bus technologies such as integrated development environment
(IDE), small computer system interface (SCSI), peripheral component
interconnect (PCI) and InfiniBand. Thus, the bus 2314 enables
communications (for example, data and instructions) to be exchanged
between system components of the computer system 2300.

[0218] Computer system 2300 also includes one or more interface devices
2316, such as input devices, output devices and combination input/output
devices. The interface devices 2316 may receive input, provide output, or
both. For example, output devices may render information for external
presentation. Input devices may accept information from external sources.
Examples of interface devices include, but are not limited to, keyboards,
mouse devices, trackballs, microphones, touch screens, printing devices,
display screens, speakers, network interface cards, and the like. The
interface devices 2316 allow the computer system 2300 to exchange
information and communicate with external entities, such as users and
other systems.

[0219] Storage system 2318 may include one or more computer-readable and
computer-writeable non-volatile and non-transitory storage media on which
computer-executable instructions are encoded that define a program to be
executed by the processor. The storage system 2318 also may include
information that is recorded on or in the media, and this information may
be processed by the program. More specifically, the information may be
stored in one or more data structures specifically configured to conserve
storage space or increase data exchange performance. The instructions may
be persistently stored as encoded signals, and the instructions may cause
a processor to perform any of the functions described herein. A medium
that can be used with various embodiments may include, for example,
optical disk, magnetic disk or flash memory, among others. In operation,
the processor 2310 or some other controller may cause data to be read
from the non-transitory recording media into another memory, such as the
memory 2312, that allows for faster access to the information by the
processor than does the storage medium included in the storage system
2318. The memory may be located in the storage system 2318 and/or in the
memory 2312. The processor 2310 may manipulate the data within the memory
2312, and then copy the data to the medium associated with the storage
system 2318 after processing is completed. A variety of components may
manage data movement between the media and the memory 2312, and the
present invention is not limited thereto.

[0220] Further, the invention is not limited to a particular memory system
or storage system. Although the computer system 2300 is shown by way of
example as one type of computer system upon which various aspects and
functions in accord with the present invention may be practiced, aspects
of the invention are not limited to being implemented on the computer
system. Various aspects and functions in accord with the present
invention may be practiced on one or more computers having different
architectures or components than that shown in the illustrative figures.
For instance, the computer system 2300 may include specially-programmed,
special-purpose hardware, such as for example, an application-specific
integrated circuit (ASIC) tailored to perform a particular operation
disclosed herein. Another embodiment may perform the same function using
several general-purpose computing devices running MAC OS System X with
Motorola PowerPC processors and several specialized computing devices
running proprietary hardware and operating systems.

[0221] The computer system 2300 may include an operating system that
manages at least a portion of the hardware placements included in
computer system 2300. A processor or controller, such as processor 2310,
may execute an operating system which may be, among others, a
Windows-based operating system (for example, Windows NT, Windows 2000/ME,
Windows XP, Windows 7, or Windows Vista) available from the Microsoft
Corporation, a MAC OS System X operating system available from Apple
Computer, one of many Linux-based operating system distributions (for
example, the Enterprise Linux operating system available from Red Hat
Inc.), a Solaris operating system available from Sun Microsystems, or a
UNIX operating systems available from various sources. The operating
system may be a mobile device or smart phone operating system, such as
Windows Mobile, Android or iOS. Many other operating systems may be used,
and embodiments are not limited to any particular operating system.

[0222] The processor and operating system together define a computing
platform for which application programs in high-level programming
languages may be written. These component applications may be executable,
intermediate (for example, C# or JAVA bytecode) or interpreted code which
communicate over a communication network (for example, the Internet)
using a communication protocol (for example, TCP/IP). Similarly,
functions in accord with aspects of the present invention may be
implemented using an object-oriented programming language, such as
SmallTalk, JAVA, C++, Ada, or C# (C-Sharp). Other object-oriented
programming languages may also be used. Alternatively, procedural,
scripting, or logical programming languages may be used.

[0223] Additionally, various functions in accord with aspects of the
present invention may be implemented in a non-programmed environment (for
example, documents created in HTML, XML or other format that, when viewed
in a window of a browser program, render aspects of a graphical-user
interface or perform other functions). Further, various embodiments in
accord with aspects of the present invention may be implemented as
programmed or non-programmed placements, or any combination thereof. For
example, a web page may be implemented using HTML while a data object
called from within the web page may be written in C++. Thus, the
invention is not limited to a specific programming language and any
suitable programming language could also be used.

[0224] A computer system included within an embodiment may perform
functions outside the scope of the invention. For instance, aspects of
the system may be implemented using an existing product. Aspects of the
system may be implemented on database management systems such as SQL
Server available from Microsoft of Seattle, Wash.; Oracle Database from
Oracle of Redwood Shores, Calif.; and MySQL from Sun Microsystems of
Santa Clara, Calif.; or integration software such as WebSphere middleware
from IBM of Armonk, N.Y. However, a computer system running, for example,
SQL Server may be able to support both aspects in accord with the present
invention and databases for sundry applications not within the scope of
the invention.

[0225] FIG. 24 is a diagram of an exemplary network environment 2400
suitable for a distributed implementation of exemplary embodiments. The
network environment 2400 may include one or more servers 2402 and 2404
coupled to one or more clients 2406 and 2408 via a communication network
2410. The network interface 2212 and the network device 2222 of the
computing device 2200 enable the servers 2402 and 2404 to communicate
with the clients 2406 and 2408 via the communication network 2410. The
communication network 2410 may include, but is not limited to, the
Internet, an intranet, a Local Area Network (LAN), a Wide Area Network
(WAN), a Metropolitan Area Network (MAN), a wireless network, an optical
network, and the like. The communication facilities provided by the
communication network 2410 are capable of supporting distributed
implementations of exemplary embodiments.

[0226] In an exemplary embodiment, the servers 2402 and 2404 may provide
the clients 2406 and 2408 with computer-readable and/or
computer-executable components or products under a particular condition,
such as a license agreement. For example, the computer-readable and/or
computer-executable components or products may include those for
providing and rendering any of the user interfaces disclosed herein. The
clients 2406 and 2408 may provide and render an exemplary graphical user
interface using the computer-readable and/or computer-executable
components and products provided by the servers 2402 and 2404.

[0227] Alternatively, in another exemplary embodiment, the clients 2406
and 2408 may provide the servers 2402 and 2404 with computer-readable and
computer-executable components or products under a particular condition,
such as a license agreement. For example, in an exemplary embodiment, the
servers 2402 and 2404 may provide and render an exemplary graphical user
interface using the computer-readable and/or computer-executable
components and products provided by the clients 2406 and 2408.

VII. EQUIVALENTS

[0228] It is to be appreciated that embodiments of the systems,
apparatuses and methods discussed herein are not limited in application
to the details of construction and the arrangement of components set
forth in the following description or illustrated in the accompanying
drawings. Exemplary systems, apparatuses and methods are capable of
implementation in other embodiments and of being practiced or of being
carried out in various ways. Examples of specific implementations are
provided herein for illustrative purposes only and are not intended to be
limiting. In particular, acts, elements and features discussed in
connection with any one or more embodiments are not intended to be
excluded from a similar role in any other embodiments. One or more
aspects and embodiments disclosed herein may be implemented on one or
more computer systems coupled by a network (e.g., the Internet).

[0229] The phraseology and terminology used herein are for the purpose of
description and should not be regarded as limiting. Any references to
embodiments or elements or acts of the systems and methods herein
referred to in the singular may also embrace embodiments including a
plurality of these elements, and any references in plural to any
embodiment or element or act herein may also embrace embodiments
including only a single element. The use herein of terms like
"including," "comprising," "having," "containing," "involving," and
variations thereof, is meant to encompass the items listed thereafter and
equivalents thereof as well as additional items. References to "or" may
be construed as inclusive so that any terms described using "or" may
indicate any of a single, more than one, and all of the described terms.
Any references front and back, left and right, top and bottom, upper and
lower, and vertical and horizontal, are intended for convenience of
description, not to limit the present systems and methods or their
components to any one positional or spatial orientation.

[0230] In describing exemplary embodiments, specific terminology is used
for the sake of clarity. For purposes of description, each specific term
is intended to, at least, include all technical and functional
equivalents that operate in a similar manner to accomplish a similar
purpose. Additionally, in some instances where a particular exemplary
embodiment includes a plurality of system elements or method steps, those
elements or steps may be replaced with a single element or step.
Likewise, a single element or step may be replaced with a plurality of
elements or steps that serve the same purpose. Further, where parameters
for various properties are specified herein for exemplary embodiments,
those parameters may be adjusted up or down by 1/20th, 1/10th, 1/5th,
1/3rd, 1/2nd, and the like, or by rounded-off approximations thereof,
unless otherwise specified. Moreover, while exemplary embodiments have
been shown and described with references to particular embodiments
thereof, those of ordinary skill in the art will understand that various
substitutions and alterations in form and details may be made therein
without departing from the scope of the invention. Further still, other
aspects, functions and advantages are also within the scope of the
invention.

[0231] Embodiments disclosed herein may be combined with other embodiments
disclosed herein in any manner consistent with at least one of the
principles disclosed herein, and references to "an embodiment," "one
embodiment," "an exemplary embodiment," "some embodiments," "some
exemplary embodiments," "an alternate embodiment," "various embodiments,"
"exemplary embodiments," and the like, are not necessarily mutually
exclusive and are intended to indicate that a particular feature,
structure, characteristic or functionality described may be included in
at least one embodiment. The appearances of such terms herein are not
necessarily all referring to the same embodiment.

[0232] Exemplary flowcharts are provided herein for illustrative purposes
and are non-limiting examples of methods. One of ordinary skill in the
art will recognize that exemplary methods may include more or fewer steps
than those illustrated in the exemplary flowcharts, and that the steps in
the exemplary flowcharts may be performed in a different order than the
order shown in the illustrative flowcharts.